# LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge
Platforms

Shahzaib Iqbal, Tariq M. Khan, Imran Razzak, Syed S. Naqvi, and Muhammad Usman
Shahzaib and Syed S. Naqi are with the Department of Electrical and Computer
Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan.Tariq M.
Khan is with the School of Computer Science and Engineering, University of New
South Wales, Sydney, NSW, Australia.Muhammad Usman is with the Department of
Computer Science and Engineering, Seoul National University, South KoreaMusaed
Alhussein and Khursheed Aurangzeb are with the Department of Computer
Engineering, College of Computer and Information, King Saud University, Saudi
Arabia.

###### Abstract

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual
block-based computational neural network tailored for medical image
segmentation on IoT and edge platforms. Conventional U-Net-based models face
challenges in meeting the speed and efficiency demands of real-time clinical
applications, such as disease monitoring, radiation therapy, and image-guided
surgery. LDMRes-Net overcomes these limitations with its remarkably low number
of learnable parameters (0.072M), making it highly suitable for resource-
constrained devices. The model’s key innovation lies in its dual multi-
residual block architecture, which enables the extraction of refined features
on multiple scales, enhancing overall segmentation performance. To further
optimize efficiency, the number of filters is carefully selected to prevent
overlap, reduce training time, and improve computational efficiency. The study
includes comprehensive evaluations, focusing on segmentation of the retinal
image of vessels and hard exudates crucial for the diagnosis and treatment of
ophthalmology. The results demonstrate the robustness, generalizability, and
high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool
for accurate and rapid medical image segmentation in diverse clinical
applications, particularly on IoT and edge platforms. Such advances hold
significant promise for improving healthcare outcomes and enabling real-time
medical image analysis in resource-limited settings.

###### Index Terms:

Medical Image Segmentation, Retinal Features Segmentation, Light-weight Deep
Networks, Dual Multiscale Residual Block

##  I Introduction

Retinal image analysis is crucial for ophthalmologists in diagnosing
blindness-causing diseases such as glaucoma, diabetic retinopathy (DR), and
age-related macular degeneration (AMD), and [1, 2, 3]. DR is one of the
primary causes of preventable blindness. Franklin et al. [4] have stated that
the characteristics of retinal blood vessels, such as slope,
neovascularization, and curvature, are essential in the diagnosis of retinal
diseases. Segmentation of medical images plays a crucial role in understanding
image content and identifying areas of injury. It serves as the foundation for
medical image analysis techniques such as medical image registration and 3D
reconstruction and also has a significant impact on clinical diagnosis and
treatment [5]. There has been an increase in the use of deep learning as a key
contribution to the field of medical image analysis. Deep learning-based
medical image segmentation has become a popular research area. Many
conventional semantic segmentation models, such as Fully Convolutional
Networks (FCN) [6] and U-Net, are based on pixel-level details [7, 8, 9, 10].

Segmentation of medical images is a crucial task that requires the utilization
of encoders and decoders. In this domain, widely recognized encoders,
including VGG and ResNet, are renowned for their exceptional feature
extraction capabilities [11, 12, 13, 14]. Additionally, other techniques such
as multiscale grouping [15], dilated convolution [16], and attention
mechanisms [17, 18, 19] are employed to extract semantic information from
medical images. Additionally, U-Net is a widely used backbone architecture for
medical image segmentation due to its efficient skip connections that augment
low-level features [20, 9, 21]. However, skip connections may also result in
information redundancy [22, 23, 24].

Figure 1: The illustration of the proposed LDMRes-Net architecture. The
proposed architecture uses dual multi-residual blocks which is basically a
combination of a multi-residual block within the residual block.

In addition, the speed of segmentation is a crucial factor when using medical
image segmentation models for clinical treatment. U-Net-based models are not
suitable for several clinical applications, such as live disease monitoring,
adaptive radiotherapy, and image-guided surgery, due to their base model
complexity in comparison to segmentation performance. In practical terms,
networks that are less complex and computationally efficient are required
without compromising segmentation accuracy.

In this paper, we present an innovative solution geared towards real-time
systems, focusing on edge platforms and IoT devices. The proposed approach
introduces the Lightweight Dual Multiscale Residual Block-based Convolutional
Neural Network (LDMRes-Net), specifically tailored for retinal feature
segmentation. The LDMRes-Net is a novel and lightweight dual multi-residual
CNN architecture that excels in computational efficiency, boasting a mere
0.072M learnable parameters. Remarkably, this model surpasses its counterparts
in accuracy without sacrificing performance. Its efficiency makes it highly
suitable for resource-constrained devices, such as edge platforms and IoT
devices.

At the core of LDMRes-Net lies a unique dual multiresidual block, meticulously
designed to enhance both robustness and efficiency. This block capitalizes on
refined feature extraction at multiple scales, significantly improving the
model’s ability to capture intricate details. Additionally, careful
optimization of the number of filters utilized in LDMRes-Net prevents filter
overlap, a common issue that could increase computational demands and training
times. Consequently, the network’s training time is substantially reduced,
further reinforcing the practicality of LDMRes-Net for real-world
applications.

In summary, the key contributions of this study are as follows.

  * •

We propose a lightweight dual multiresidual CNN for retinal features
segmentation, which comprises an exceptionally low number of learnable
parameters (0.072M).

  * •

A dual multi-residual block is proposed to enhance the robustness and
efficiency of the approach by leveraging the benefits of refined feature
extraction and multiple scales.

  * •

We used the optimal number of filters to prevent filter overlap significantly
reducing the training time of the proposed LDMRes-Net.

  * •

We tested our proposed network on multiple aspects of retinal images (such as
retinal vessels, and hard exudates) to assess its robustness and
generalizability.

The remaining manuscript is organized in the following way: Section II
provides recent relevant studies on retinal feature segmentation methodologies
and lightweight medical image segmentation models. The details of the proposed
model LDMRes-Net are provided in Section III. The experimental setup is
elaborated as well as the comprehensive experiments and results of LDMRes-Net
are presented in Section IV-B. Finally, Section VI summarizes the primary
findings and conclusions of the proposed work.

Figure 2: Connectivity graph of dual multi-residual block used in LDMRes-Net.

##  II Related Work

Medical image segmentation is an essential technique in various medical
imaging applications, as it facilitates accurate diagnosis, personalized
treatment planning, improved visualization, quantitative analysis, and less
invasive medical procedures. U-Net architecture has undergone various
adaptations and improvements in the literature, demonstrating its
effectiveness for different visual analytics applications through module
redesign and network construction [25]. For instance, Milletari et al. [26]
expanded U-Net to 3D image segmentation based on a fully convolutional
volumetric neural network while preserving the vanilla internal structures.
Contrary to the strategy used by U-Net, the method proposed by Mehta et al.
[27] involves the integration of input features of varying scales at different
stages. This process enables the acquisition of multilevel image information
through a series of downsampling and upsampling layers. On a similar note,
Zhou et al. [9] proposed U-Net++, an approach that more efficiently captures
fine-grained details through the use of densely stacked skip connections.
Additionally, Oktay et al. [28] presented Attention U-Net, which incorporates
extra branches into the attention mechanism to integrate skipped and decoded
data.

In recent years, lightweight segmentation networks have gained attention in
image segmentation challenges. SegNAS3D [29] introduced a network search to
optimize the 3D segmentation structure and significantly reduce the complexity
of the model. RefineNet [30] was used as the lightweight backbone network by
Nekrasov et al. [31] to improve the performance of the model. IC-Net [32] is a
real-time semantic segmentation network that uses an image cascade with branch
training to accelerate model convergence. BiSeNet [33] is a lightweight model
based on a dual-branch topology, employing multiple paths to refine spatial
information. DMFNet [34] partitions channels into numerous groups and employs
a weighted three-dimensional expanded convolution. This approach ultimately
results in a decrease in the parameter count while simultaneously enhancing
the accuracy of inference. Xception [35] and MobileNets [36, 37] use deep
separable convolution to improve the inference speed. The Dense-Inception
U-Net [38] uses a compact encoder with a lightweight Inception backbone and a
dense module to capture high-level semantic information. This architecture is
designed for medical image segmentation tasks.

TABLE I: The details of retinal fundus image datasets (publicly available)
used for the performance evaluation of LDMRes-Net

Feature Dataset Number of Images Orignal FOV Resized Patch Size Training
Details Image Image Training Testing Total Resolution Resolution Blood Vessels
DRIVE [39] 20 20 40 $565\times 584$ 35 $640\times 640$ - Image Level CHASE_DB
[40] 20 - 20 $960\times 990$ 30 STARE [41] 28 - 28 $605\times 700$ 45 HRF [42]
15 30 45 $2336\times 3504$ 60 ARIA [43] 120 22 142 $584\times 768$ 50 IOSTAR
[44] - 30 - $1168\times 1752$ - ORVS[45] 42 7 49 $1444\times 1444$ 45 Hard
Exudates IDRiD [46] 54 27 81 $2848\times 4288$ - $3200\times 4480$ $640\times
640$ Patch Level

ShuffleNet [47, 48] applies group convolution and channel shuffling techniques
to decrease computational expenses compared to more complex models.
Researchers have explored the development of lightweight segmentation networks
specifically for medical images, as Iqbal et al. [49]. However, creating
networks that have low model complexity, high inference speed, and excellent
performance still poses a challenge in most medical image segmentation tasks.
nnU-Net [50] improves network adaptability by preprocessing data and
postprocessing segmentation results, but the model parameters increase with
this approach. U-Net++ [9] produces high segmentation performance with minimal
parameters, but its inference time is not taken into account. A lightweight
V-Net [51] uses depth and point convolution to ensure segmentation accuracy
and use fewer parameters. However, it does not speed up the inference process.
Furthermore, Tarasiewicz et al. [52] developed Lightweight U-Nets that can
accurately delineate brain tumors from multimodal MRIs and trained several
skinny networks in all image planes. PyConvU-Net [19] increases segmentation
accuracy while using fewer parameters by replacing all traditional U-Net’s
convolutional layers with pyramidal convolution. However, its inference speed
is low. G-Net Light [49], PLVS-Net [53], and MKIS-Net[54] are effective CNN
architectures for segmenting retinal blood vessels, while also being
lightweight.

Transformers, unlike CNN-inspired architectures, are typically used for
machine translation but have recently found applications in computer vision
tasks. Vision transformers (ViTs) [55] cascade numerous transformer layers,
instead of CNN-inspired architectures, and have received significant research
interest. Several subsequent techniques have been developed that expand on
ViTs, and many semantic segmentation algorithms [56, 57, 58, 59] based on ViT
backbones have achieved promising results due to the powerful representations
learned from pretrained backbones. However, ViTs are computationally
expensive. To overcome this challenge, lightweight CNNs can be used, which
offer several benefits for medical image segmentation, such as faster
processing times, lower memory requirements, improved portability, lower
computational cost, and lower energy consumption. Therefore, the use of
lightweight methods for medical image segmentation can offer numerous
advantages in terms of speed, efficiency, portability, and cost-effectiveness,
making them an appealing option for a wide range of applications.

TABLE II: A performance comparison between LDMRes-Net and several alternative
methods was conducted using the DRIVE [39] dataset.

Method Performance Measures in (%) Se Sp Acc AUC F1 DeepVessel [60] 76.12
97.68 95.23 97.52 - Orlando et al. [61] 78.97 96.84 94.54 95.06 - Att UNet
[28] 79.46 97.89 95.64 97.99 82.32 H-DenseUNet [62] 79.85 98.05 95.73 98.10
82.79 BTS-DSN [63] 78.00 98.06 95.51 97.96 82.08 DUNet [64] 79.84 98.03 95.75
98.11 82.49 M2U-Net [65] - - 96.30 97.14 80.91 Bio-Net [66] 82.20 98.04 96.09
82.06 98.26 CC-Net [67] 76.25 98.09 95.28 96.78 - CTF-Net [68] 78.49 98.13
95.67 97.88 82.41 CSU-Net [69] 80.71 97.82 95.65 98.01 82.51 OCE-Net [70]
80.18 98.26 95.81 98.21 83.02 Wave-Net [71] 81.64 97.64 95.61 - 82.54
LightEyes [72] - - - 97.96 - G-Net Light [49] 81.92 98.29 96.86 - 82.02
Proposed LDMRes-Net 83.58 98.32 97.02 98.51 83.09

Figure 3: Segmentation performance on several test images from the DRIVE [39]
dataset. From left to right: the input images (from top to bottom: images 1,
2, 4, 16 from the dataset), the ground truth and the results of BCD-Unet [73],
MultiResNet [74], SegNet [75], UNet++ [76], G-Net Light [49], and LDMRes-Net.
True positives are displayed in green, false positive pixels are displayed in
red, and blue pixels represent false negatives. TABLE III: Performance
comparison of LDMRes-Net and a number of alternatives on the STARE [41]
dataset.

Method | Performance Measures in (%)
---|---
Se | Sp | Acc | AUC | F1
Orlando et al. [61] | 76.80 | 97.38 | 95.19 | 95.70 | -
Att UNet [28] | 77.09 | 98.48 | 96.33 | 97.00 | -
BTS-DSN [63] | 82.01 | 98.28 | 96.60 | 98.72 | 83.62
DUNet [64] | 78.92 | 98.16 | 96.34 | 98.43 | 82.30
CC-Net [67] | 80.67 | 98.16 | 96.32 | 98.33 | 81.36
OCE-Net [70] | 80.12 | 98.65 | 96.72 | 98.76 | 83.41
Wave-Net [71] | 79.02 | 98.36 | 96.41 | - | 81.40
LightEyes [72] | - | - | - | 98.29 | -
G-Net Light [49] | 81.70 | 98.53 | 97.30 | - | 81.78
Proposed LDMRes-Net | 84.07 | 98.75 | 97.64 | 98.72 | 84.24

Figure 4: Segmentation performance on several test images from the STARE [41]
dataset. From left side to right side: the input images (from top to bottom:
images 2, 3, 4, 5 from the dataset), ground truth, and the results of BCD-Unet
[73], MultiResNet [74], SegNet [75], UNet++ [76], G-Net Light [49], and
LDMRes-Net. True positives are displayed in green, false positive pixels are
displayed in red, and blue pixels represent false negatives. Figure 5:
Segmentation results of representative test images from the CHASE_DB [40]
dataset. From left to right: the input images, ground truth, and the results
of SegNet [75], VessSeg [77], G-Net Light [49], and LDMRes-Net. The ”red” and
”blue” represents false positive and false negatives respectively. Figure 6:
Visual performance of LDMRes-Net of retinal vessel segmentation on different
datasets having different image resolutions.$1^{st}$ row is input RGB images,
$2^{nd}$ row is corresponding ground truth images and the $3^{rd}$ row is
segmentation output images of LDMRes-Net. TABLE IV: A performance comparison
between LDMRes-Net and several alternative methods was conducted using the
CHASE_DB [40] dataset.

Method | Performance Measures in (%)
---|---
Se | Sp | Acc | AUC | F1
DeepVessel [60] | 74.12 | 97.01 | 96.09 | 97.90 | -
Orlando et al. [61] | 75.65 | 96.55 | 94.67 | 94.78 | -
Att UNet [28] | 80.10 | 98.04 | 96.42 | 98.40 | 80.12
BTS-DSN [63] | 78.88 | 98.01 | 96.27 | 98.40 | 79.83
DUNet [64] | 77.35 | 98.01 | 96.18 | 98.39 | 79.32
M2U-Net [65] | - | - | 97.03 | 96.66 | 80.06
OCE-Net [70] | 81.38 | 98.24 | 96.78 | 98.72 | 81.96
Wave-Net [71] | 82.83 | 98.21 | 96.64 | - | 83.49
LightEyes [72] | - | - | - | 98.20 | -
G-Net Light [49] | 82.10 | 98.38 | 97.26 | - | 80.48
Proposed LDMRes-Net | 85.95 | 98.88 | 97.55 | 98.61 | 81.94

TABLE V: A performance comparison between LDMRes-Net and several alternative
methods was conducted using the HRF [42] dataset.

Model | Performance Measures in (%)
---|---
Se | Sp | Acc | AUC | F1
U-net [20] | - | - | 95.87 | 83.05 | 72.39
Orlando [78] | 78.74 | 95.84 | - | - | 95.84
DUNet [64] | 74.64 | 98.74 | 96.51 | - | -
CS-Net [79] | - | - | 95.66 | 82.32 | 71.04
CogSeg [80] | - | - | 96.22 | 84.31 | 74.75
SuperVessel [81] | - | - | 96.54 | 85.06 | 76.74
Proposed LDMRes-Net | 80.78 | 98.39 | 97.11 | 89.88 | 80.20

TABLE VI: Performance of LDMRes-Net ARIA [43], IOSTAR [44], and ORVS [45]
datasets.

Dataset | Performance Measures in (%)
---|---
Se | Sp | Acc | AUC | F1
ARIA [43] | 74.18 | 97.42 | 95.81 | 96.00 | 71.49
IOSTAR [44] | 81.52 | 98.25 | 96.57 | 97.30 | 82.81
ORVS[45] | 78.97 | 98.63 | 97.55 | 91.20 | 77.98

##  III Proposed Method

We propose a lightweight dual multiresidual CNN for retinal feature
segmentation. The overall architecture of the proposed network is presented in
(Fig.1). The proposed network is a lightweight encoder-decoder network that
uses the advantages of multi-scale information extraction and feature
refinement from a novel dual multi-scale residual block. The schematic
connectivity of the dual multiresidual block used in LDMRes-Net is illustrated
in (Fig. 2). The dual MultiRes blocks, which combine two multiscale residual
blocks, offer several benefits for medical image segmentation tasks. These
benefits include enhanced feature extraction, minimized overfitting, superior
resolution adaptability, deeper network structure, accelerated convergence,
improved handling of class imbalances, and optimized gradient flow. By
utilizing two parallel multiscale residual blocks with different scale
parameters, dual multiscale residual blocks can capture a wider range of
features across different input image scales, resulting in diverse and
complementary feature learning. Furthermore, the integration of features from
different multiscale residual blocks and the use of multiple scales allows the
network to generalize more effectively and reduce overfitting, particularly
when dealing with limited training data. Dual multiscale residual blocks are
also more resistant to fluctuations in image resolution, making the model more
versatile and suitable for a broader range of segmentation tasks with varying
resolutions. Additionally, the dual configuration of the multiscale residual
block encourages faster convergence during training, leading to more efficient
training and faster convergence to an optimal solution. Finally, the dual
multiscale residual block can improve the handling of class imbalance, which
is frequently encountered in retinal image feature segmentation tasks, by
learning features at different scales, making the network better prepared to
manage classes with different sizes and densities. The parallel arrangement of
a dual multiscale residual block can also boost gradient flow during
backpropagation, aiding in training deeper networks and mitigating the
vanishing gradient issue. In general, dual multiscale residual blocks offer a
robust and efficient approach to image segmentation tasks by taking advantage
of multiple scales and refined feature extraction.

To start with, let $l^{n\times n}=\beta_{n}(f^{n\times n}(*))$ be defined as
the convolution operation $f^{n\times n}$ followed by the batch normalization
$beta_{n}$ on any given input image. where $n\times n$ is the size of the
kernel. Let the layer input be defined as $X_{in}$, where
$X\in\mathbb{R}^{C\times H\times W}$. $f_{1}$ is the output of the function in
which $l^{1\times 1}$ is applied followed by an activation function calculated
in (Eq. 1).

$f_{1}=\Re(l^{1\times 1}(X_{in}))$ (1)

Here $\Re$ denotes the ReLU operation. Downsampled feature maps $F_{in}$,
where $F_{in}\in\mathbb{R}^{C\times\frac{H}{2}\times\frac{W}{2}}$ are then
computed in (Eq. 2) by applying $l^{1\times 1}$ followed by $l^{3\times 3}$,
an activation function, and the maximum grouping operation to the input
$f_{1}$. $M_{p}$ is the max pooling operation.

$F^{enc}=M_{p}\left(\Re\left(l^{3\times 3}\left(l^{1\times
1}\left(f_{1}\right)\right)\right)\right)$ (2)

Once the feature maps are down-sampled, they are fed as the input of the dual
multiscale residual block, where feature extraction is performed and refined
with multiscale information. The $\Im_{1}^{enc}$ is the output of the dual
multiscale residual block given in (Eq. 3), where $k=2\times i-1$.

$\Im_{1}^{enc}=\beta_{N}(f^{1\times
1}(F_{in}))+\sum_{i=1}^{2}\beta_{N}(f^{k\times k}(S_{2}^{enc}))$ (3)

$S_{2}^{enc}=\Re\left(\beta_{N}(f^{1\times
1}(\breve{s}_{1}))+S_{1}^{enc}\right)$ (4)

$S_{1}^{enc}=\Re\left(\sum_{k=1}^{2}\beta_{N}\left(f^{k\times
k}\left(l_{2}\right))\right)\right)$ (5)

$S_{2}^{enc}$ and $\S_{1}^{enc}$ are the intermediate outputs of the addition
layers of the dual multiscale residual block and are computed as (Eqs. 4-5).

Once feature extraction is performed and refined at multiscale, information is
fed to the ReLU layer followed by the max-pooling operation to reduce the
spatial dimensions of the feature maps. The output of the second dual
multiscale residual block $\Im_{2}(out)$, where
$\Im_{2}(out)\in\mathbb{R}^{C\times\frac{H}{4}\times\frac{W}{4}}$ as given in
(Eq. 1-5). In the end, the final output of the encoder $\Im_{out}^{enc}$,
where $\Im_{out}^{enc}\in\mathbb{R}^{C\times\frac{H}{8}\times\frac{W}{8}}$ is
computed by the ReLU layer followed by the max-pooling operation and the dual
multiscale residual block.

On the decoder side, the same process is applied to the encoder features. The
only difference on the decoder is that the max pooling $M_{p}$ layers are
replaced with the upsampling layer. The output of the decoder blocks is
computed as given in (Eq.6-8).

$\Im_{1}^{dec}=\beta_{N}(f^{1\times
1}(\Im_{out}^{enc}))+\sum_{i=1}^{2}\beta_{N}(f^{k\times k}(S_{2}^{dec}))$ (6)

$S_{2}^{dec}=\Re\left(\beta_{N}(f^{1\times
1}(S_{1}^{dec}))+S_{1}^{dec}\right)$ (7)

$S_{1}^{dec}=\Re\left(S_{1}^{enc}+\sum_{k=1}^{2}\beta_{N}\left(f^{k\times
k}\left(\Im_{out}^{Enc}\right))\right)\right)$ (8)

$\Im_{out}^{dec}=\Gamma\left(\Re\left(l^{3\times 3}\left(f_{1}+l^{3\times
3}\left(F^{dec}\right)\right)\right)\right)$ (9)

The output of the decoder side $\Im_{out}^{dec}\in\mathbb{R}^{C\times H\times
W}$ is computed as given in (Eq.9). where, the $\Gamma$ is the softmax
operation. Finally, the dice-pixel classification layer is used to obtain the
binary segmentation.

TABLE VII: Performance comparison of hard exudates segmentation of proposed
LDMRes-Net with a number of alternatives on the IDRiD [46] data set.

Method | Performance Measures in (%)
---|---
Se | Sp | Acc | F1
CNN Based Model [82] | 87.58 | 98.73 | 86.32 | 85.39
Deep Residual Network [83] | 96.30 | 93.04 | 94.08 | 95.17
Semi-Supervised Method [84] | 94.38 | 94.75 | 96.73 | 95.63
L-SegNet [85] | 81.65 | 76.13 | 85.64 | 89.71
Deep Membrane System [86] | 77.91 | 96.75 | 97.31 | 96.52
CNN Based Model [87] | 85.73 | 85.62 | 90.17 | 88.93
Modified U-Net [88] | 93.35 | 94.82 | 95.74 | 96.81
Proposed LDMRes-Net | 96.33 | 98.98 | 98.35 | 95.05

In this work, we have used the training approach using composite constraints
to eliminate unnecessary filters and reduce their impact on the network
learning process by promoting the sparsity proposed in [89].

##  IV Experiments and Results

In this section, we demonstrate the effectiveness of our approach for
segmenting retinal blood vessels on publicly available data sets and perform a
comparative analysis of our results against those achieved by previously
published methods.

###  IV-A Datasets

The effectiveness of LDMRes-Net was evaluated through segmentation experiments
in five different retinal image datasets (Table I). The DRIVE [39], STARE
[41], and CHASE_DB [40], [42], ARIA [43], IOSTAR [44], and OVRS data sets were
used to evaluate the segmentation of the retinal blood vessels and IDRiD [46]
for the segmentation of hard exudates. Table I shows the details of the above-
mentioned data sets that are used for the performance evaluation of LDMRes-
Net.

###  IV-B Experimental Setup

In this study, the ADAM optimizer was used with an initial learning rate set
to $0.00002$ and a decay rate of $0.90$. Before the training process began,
the images of each data set were standardized to dimensions of $640\times 640$
pixels and then normalized using z-score statistics. This normalization
process helps to ensure a more efficient learning process by keeping the input
values within a similar range.

Data augmentation techniques were also incorporated to enhance the robustness
of the model to various transformations. Techniques such as contrast
enhancement, brightness adjustment, random flipping, and random rotation
(ranging from 1 to 360 degrees) were used. These techniques artificially
increased the number of training images, providing a more diverse set of data
from which the model can learn.

For the specific task of segmentation of exudates, the experiments were
implemented using a patch-based approach. Training images from the IDRiD
dataset [46] were divided into non-overlapping patches of $640\times 640$
pixels. Then these generated patches were partitioned into training and
validation sets, adhering to a 80%: 20% split. This approach helps to ensure
that the model is able to generalize well to unseen data and not just perform
well on the data on which it was trained.

###  IV-C Evaluation Criteria

The evaluation of the segmentation results was performed by comparing them
with their corresponding ground-truth images. Each pixel in the output image
was classified as correctly segmented foreground pixels ($T_{P}$: true
positives) or background pixels ($T_{N}$: true negatives), or as wrongly
segmented foreground pixels ($F_{P}$: false positives) or background pixels
($F_{N}$: false negatives).

Sensitivity (Se) is the ratio of ($T_{P}$) to the total number of actual
positives ($T_{P}$\+ $F_{N}$). It represents the ability of the method to
detect all positive samples correctly. It can be formulated as

$S_{e}=\frac{T_{P}}{T_{P}+F_{N}},$ (10)

Specificity (Sp) is the ratio of ($T_{N}$) to the total number of actual
negatives ($T_{N}$\+ $F_{P}$). It represents the ability of the method to
correctly identify all negative samples. It can be formulated as

$S_{p}=\frac{T_{N}}{T_{N}+F_{P}}.$ (11)

Accuracy (Acc) is the proportion of correctly identified pixels ($T_{P}$\+
$T_{N}$) to all pixels in the image. Displays the total effectiveness of the
method. It can be stated as follows.

$A_{cc}=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}}.$ (12)

The $F_{1}$ score, or Dice similarity coefficient (DSC), is another widely
used metric to evaluate model performance. It is calculated as the harmonic
mean of precision and recall, and can be expressed as:

$F_{1}=\frac{2\times T_{P}}{(2\times T_{P})+F_{P}+F_{N}}.$ (13)

The area under the curve (AUC) is a metric used in receiver operating
characteristic (ROC) analysis. The ROC curve plots the true positive rate (Se)
against the false positive rate (1+ $S_{p}$)(1-Sp) for different
classification thresholds. AUC measures the overall ability of the method to
distinguish between positive and negative samples.

$AUC=1-\frac{1}{2}\left(\frac{F_{P}}{F_{P}+T_{N}}+\frac{F_{N}}{F_{N}+T_{P}}\right).$
(14)

Figure 7: Hard exudates segmentation results of LDMRes-Net on representative
test images from the IDRiD [46] dataset. $1^{st}$ row shows the RGB images,
$2^{nd}$ row shows the corresponding ground truths, and $3^{rt}$ row shows the
LDMRes-Net segmentation results. True positives are displayed in green, false
positive pixels are displayed in red, and blue pixels represent false
negatives.

###  IV-D LDMRes-Net Generalization Analysis

It is important to develop a method with strong generalization ability due to
the variations in imaging equipment across different hospitals. The
generalization ability of a model demonstrates its capacity to adapt to new
data effectively. In this section, we demonstrate the generalization ability
of the proposed LDMRes-Net by evaluating its segmentation performance on
multiple datasets. The datasets utilized in this comparison are DRIVE [39],
STARE [41], CHASE_DB1 [40], HRF [42], ARIA [43], IOSTAR [44], ORVS [45], and
IDRiD [46].

For a comprehensive understanding of the performance, we also reference prior
studies that have compared numerous supervised methodologies. In particular,
we assess the performance of LDMRes-Net against U-Net [90], and SegNet [75].
These two deep-learning architectures are popular and frequently serve as
benchmark models within the image segmentation community.

Tables II, IV, and III provide the quantitative evaluation for our proposed
LDMRes-Net alongside several other methodologies. As the tables illustrate,
LDMRes-Net surpasses all other methods across all metrics, including
sensitivity, specificity, accuracy, Area Under the Curve (AUC), and the
$F1$-score.

Specifically, on the DRIVE dataset [39], LDMRes-Net achieved performance
metrics of 83.48%, 98.77%, 96.97%, 98.46%, and 83.81% for sensitivity,
specificity, accuracy, AUC, and $F1$-score, respectively. Similarly, on the
STARE dataset [41], LDMRes-Net achieved values of 85.19%, 98.651%, 97.59%,
99.06%, and 84.22% for sensitivity, specificity, accuracy, AUC, and
$F1$-score, respectively. On the CHASE_DB1 dataset, LDMRes-Net attained scores
of 83.29%, 98.34%, 97.31%, 98.82%, and 82.10% for sensitivity, specificity,
accuracy, AUC, and $F1$-score, respectively. These findings underscore the
superior performance of LDMRes-Net in comparison to other state-of-the-art
(SOTA) networks. It’s worth noting that among the alternative methods, there
is no apparent trend or consistent superior performance in terms of
specificity, AUC, or sensitivity.

We present a visual comparison of the results obtained by LDMRes-Net and
alternative methods on the DRIVE [39], STARE [41], and CHASE_DB [40] datasets
in Figures 3, 4, and 5. The results obtained on the DRIVE dataset (Fig.3)
illustrate that LDMRes-Net notably reduces false positives on small vessels in
comparison to current SOTA methods. U-Net-based variants, for instance, seem
to struggle with accurately delineating the boundaries, while SegNet [75]
tends to generate false tiny vessels across most images. The BCD-Unet [73]
technique, on the other hand, seems to overlook crucial vessel information. In
contrast, LDMRes-Net effectively captures this information while also
minimizing the generation of false vessel information. Upon applying these
methods to the STARE dataset (Fig.4), it’s evident that alternative techniques
tend to produce more false positives, especially around retinal boundaries,
optic nerves, and small vessels. The proposed LDMRes-Net, however, proves to
be more resilient to these artifacts in the images. Similar findings are
observed when the LDMRes-Net method is applied to the CHASE_DB dataset, as
demonstrated in Fig. 5. This further establishes the robustness and efficacy
of the LDMRes-Net method across various datasets and image conditions.

Table V presents a quantitative comparison of LDMRes-Net and other methods
applied on the HRF dataset [42]. The LDMRes-Net outperforms other methods by
achieving an average sensitivity, specificity, accuracy, AUC, and
$F_{1}$-score of 80.78%, 98.39%, 97.11%, 89.88%, and 80.20%, respectively.
Additionally, the performance of LDMRes-Net is evaluated on the ARIA [43],
IOSTAR [44], and ORVS [45] datasets, as showcased in Table VI. Fig.6 provides
a visual representation of the vessel segmentation results obtained by our
proposed LDMRes-Net on a sample image from each retinal dataset. The figure
includes the corresponding ground truth and related retinal images for
comparison. Despite the variations in vessel orientations and resolutions
across the datasets, the figure clearly demonstrates the robust performance of
LDMRes-Net in vessel segmentation tasks.

Table VII displays a quantitative comparison of the performance of LWMB-Net
and other SOTA methods on the IDRiD dataset [46] specifically for the
segmentation of hard exudates. LDMRes-Net attains an average sensitivity,
specificity, accuracy, AUC, and $F1$ score of 94.35%, 98.98%, 98.35%, 98.49%,
and 95.05%, respectively. LWMB-Net outperforms other methods in every
performance metric, except for the $F1$-score, where it ranks second best
among the discussed methods. The Deep Membrane System [86] boasts a higher
$F1$-score than the LWMB-Net, although it falls behind in other performance
measures. Fig.7 visually illustrates the segmentation results for hard
exudates, as achieved by LDMRes-Net on representative test images from the
IDRiD dataset. Notably, LDMRes-Net attains competitive results in the presence
of other lesions and features in the images, all while utilizing a minimal
number of learnable parameters. This highlights the model’s efficiency and
effectiveness in this complex task.

Figure 8: ROC curves of the proposed LDMRes-Net on different retinal image
datasets: (a) DRIVE [39], (b) STARE [41], (c) CHASE_DB [40], (d) [42], (e)
ARIA [43], (f) IOSTAR [44], (g) ORVS[45], and (h) IDRiD [46] (hard exudates).

The area under the ROC curves is a crucial metric for evaluating the
performance of algorithms. In this context, to assess the impact of our
proposed LDMRes-Net, we have conducted an analysis of the area under the ROC
curves. Fig. 8 illustrates the ROC curves of our proposed method when applied
to vessel segmentation on a variety of datasets. These datasets include DRIVE
[39], STARE [41], CHASE_DB [40], HRF [42], IOSTAR [44], ARIA [43], and ORVS
[45]. The ROC curves for the segmentation of hard exudates on the IDRiD [46]
dataset is also included. This comprehensive evaluation helps us understand
the performance and reliability of the proposed LDMRes-Net across different
tasks and datasets.

##  V Computational Requirements Analysis

In this section, a thorough analysis of the computational requirements of the
proposed LDMRes-Net method is presented, with a comparison to state-of-the-art
(SOTA) methods. Table VIII is provided to offer a detailed comparison,
emphasizing the efficiency and effectiveness of the LDMRes-Net approach.

###  V-A Comparison with SOTA Lightweight Networks

Table VIII provides an extensive comparison between the LDMRes-Net method and
various well-established state-of-the-art (SOTA) methods. The comparison
primarily focuses on two crucial aspects: the number of learnable parameters
and the size of the models, measured in megabytes (MB).

For comparative analysis, we include several well-established methods, namely
MobileNet-V3-small [91], ERFNet [92], MultiRes UNet [93], VessNet [94], PLVS-
Net [53], M2U-Net [65], and G-Net Light [49]. These methods have been widely
recognized in the field and are commonly used as benchmarks for performance
evaluation.

###  V-B Computational Efficiency of LDMRes-Net

It should be noted that the LDMRes-Net method demonstrates remarkable
computational efficiency, primarily evident in its substantially reduced
number of learnable parameters. With a mere 0.072 million parameters, the
LDMRes-Net surpasses other methods in terms of parameter efficiency.
Importantly, this enhanced efficiency does not compromise the top-tier
performance expected in medical imaging analyzes. The LDMRes-Net method
achieves a harmonious balance between computational efficiency and exceptional
segmentation results.

In addition, the LDMRes-Net model exhibits a remarkably compact size,
requiring a mere 0.58 megabytes of storage. This compactness facilitates the
effortless deployment and utilization of the LDMRes-Net method in real-world
clinical settings. The reduced size of the model contributes to improved
efficiency and effectiveness in medical imaging analyzes, allowing for
seamless integration and practical implementation. The compact size of the
LDMRes-Net model offers advantages in terms of storage requirements, enabling
greater accessibility and utilization on various computing platforms.

###  V-C Practical Implications

The computational efficiency of the LDMRes-Net method has significant
practical implications. Reducing the number of learnable parameters and
reducing the size of the model results in a number of advantages, including
shorter training times and reduced memory requirements. Thus, LDMRes-Net is
ideally adapted for environments with limited computational resources.

Moreover, the lightweight nature of LDMRes-Net enables its seamless deployment
on various hardware platforms, including edge devices with restricted
computational capabilities. This versatility empowers medical professionals to
analyze medical images in real-time, facilitating timely decision-making and
enhancing the overall efficiency of clinical workflows.

In conclusion, the complexity analysis and the comprehensive comparison
presented in Table VIII demonstrate the computational advantages of the
LDMRes-Net method. Its minimal number of learnable parameters and compact size
position it as a highly efficient solution for medical image analysis. These
characteristics enable its practical implementation in real-world clinical
settings, contributing to more efficient and effective medical imaging
analyses.

TABLE VIII: Computational requirements comparisons of the LDMRes-Net with SOTA
methods.

Method | Params (M) | Size (MB)
---|---|---
MobileNet-V3-small [91] | 2.5 | 11.0
ERFNet [92] | 2.06 | 8.0
MultiRes UNet [93] | 7.2 | -
VessNet [94] | 9.3 | 36.6
PLVS-Net [53] | 1 | 3.6
M2U-Net [65] | 0.55 | 2.20
G-Net Light [49] | 0.39 | 1.52
LDMRes-Net | 0.072 | 0.58

##  VI Conclusions

This study introduces the LDMRes-Net, a retinal image feature segmentation
model that exhibits exceptional performance with only 0.072 million learnable
parameters. The LDMRes-Net incorporates innovative dual multiscale residual
blocks, which effectively prevent feature overlap and contribute to faster
convergence. The experimental results obtained with the LDMRes-Net on various
retinal image features, such as retinal vessels and hard exudates, validate
its robustness and versatility. Notably, the LDMRes-Net outperforms other
segmentation methods in terms of retinal vessel and diabetic retinopathy (DR)
lesion segmentation. The lightweight design of the LDMRes-Net positions it as
an optimal choice for real-time disease diagnosis in clinical settings. With
its impressive performance and computational efficiency, the LDMRes-Net holds
great promise for enhancing the accuracy and efficiency of medical imaging
analyses.

## References

  * [1] K. B. Khan, A. A. Khaliq, A. Jalil, M. A. Iftikhar, N. Ullah, M. W. Aziz, K. Ullah, and M. Shahid, “A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends,” _Pattern Analysis and Applications_ , vol. 22, no. 3, pp. 767–802, 2019.
  * [2] S. Iqbal, T. M. Khan, K. Naveed, S. S. Naqvi, and S. J. Nawaz, “Recent trends and advances in fundus image analysis: A review,” _Computers in Biology and Medicine_ , p. 106277, 2022.
  * [3] R. Imtiaz, T. M. Khan, S. S. Naqvi, M. Arsalan, and S. J. Nawaz, “Screening of glaucoma disease from retinal vessel images using semantic segmentation,” _Computers & Electrical Engineering_, vol. 91, p. 107036, 2021.
  * [4] S. W. Franklin and S. E. Rajan, “Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images,” _Biocybernetics and Biomedical Engineering_ , vol. 34, no. 2, pp. 117–124, 2014.
  * [5] T. M. Khan, M. Mehmood, S. S. Naqvi, and M. F. U. Butt, “A region growing and local adaptive thresholding-based optic disc detection,” _Plos one_ , vol. 15, no. 1, p. e0227566, 2020.
  * [6] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2015, pp. 3431–3440.
  * [7] S. Zhou, D. Nie, E. Adeli, J. Yin, J. Lian, and D. Shen, “High-resolution encoder-decoder networks for low-contrast medical image segmentation,” _IEEE Transactions on Image Processing_ , vol. 29, pp. 461–475, 2019.
  * [8] K. Naveed, F. Abdullah, H. A. Madni, M. A. Khan, T. M. Khan, and S. S. Naqvi, “Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3d filter,” _Diagnostics_ , vol. 11, no. 1, p. 114, 2021.
  * [9] Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: Redesigning skip connections to exploit multiscale features in image segmentation,” _IEEE Transactions on Medical Imaging_ , vol. 39, no. 6, pp. 1856–1867, 2019.
  * [10] J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, “TransUNet: Transformers make strong encoders for medical image segmentation,” _arXiv:2102.04306_ , 2021.
  * [11] Z. Gu, J. Cheng, H. Fu, K. Zhou, H. Hao, Y. Zhao, T. Zhang, S. Gao, and J. Liu, “CE-Net: Context encoder network for 2D medical image segmentation,” _IEEE Transactions on Medical Imaging_ , vol. 38, no. 10, pp. 2281–2292, 2019\.
  * [12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2016, pp. 770–778.
  * [13] J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical transformer: Gated axial-attention for medical image segmentation,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2021, pp. 36–46.
  * [14] S. S. Naqvi, N. Fatima, T. M. Khan, Z. U. Rehman, and M. A. Khan, “Automatic optic disk detection and segmentation by variational active contour estimation in retinal fundus images,” _Signal, Image and Video Processing_ , vol. 13, pp. 1191–1198, 2019.
  * [15] W. Liu, A. Rabinovich, and A. C. Berg, “ParseNet: Looking wider to see better,” _arXiv:1506.04579_ , 2015.
  * [16] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_ , vol. 40, no. 4, pp. 834–848, 2017.
  * [17] N. Q. K. Le, E. K. Y. Yapp, N. Nagasundaram, and H.-Y. Yeh, “Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous fasttext N-grams,” _Frontiers in Bioengineering and Biotechnology_ , p. 305, 2019.
  * [18] Z.-L. Ni, G.-B. Bian, X.-H. Zhou, Z.-G. Hou, X.-L. Xie, C. Wang, Y.-J. Zhou, R.-Q. Li, and Z. Li, “RAUNet: Residual attention U-Net for semantic segmentation of cataract surgical instruments,” in _International Conference on Neural Information Processing_ , 2019, pp. 139–149.
  * [19] C. Li, Y. Fan, and X. Cai, “PyConvU-Net: A lightweight and multiscale network for biomedical image segmentation,” _BMC Bioinformatics_ , vol. 22, no. 1, pp. 1–11, 2021.
  * [20] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2015, pp. 234–241.
  * [21] T. M. Khan, A. Robles-Kelly, and S. S. Naqvi, “A semantically flexible feature fusion network for retinal vessel segmentation,” in _International Conference on Neural Information Processing_. Springer, Cham, 2020, pp. 159–167.
  * [22] ——, “T-Net: A resource-constrained tiny convolutional neural network for medical image segmentation,” in _IEEE/CVF Winter Conference on Application of Computer Vision (WACV)_ , 2022, pp. 644–653.
  * [23] T. M. Khan, A. Robles-Kelly, S. S. Naqvi, and A. Muhammad, “Residual multiscale full convolutional network (rm-fcn) for high resolution semantic segmentation of retinal vasculature,” in _Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, S+ SSPR 2020, Padua, Italy, January 21–22, 2021, Proceedings_. Springer Nature, 2021, p. 324.
  * [24] T. M. Khan, A. Robles-Kelly, and S. S. Naqvi, “Rc-net: A convolutional neural network for retinal vessel segmentation,” in _2021 Digital Image Computing: Techniques and Applications (DICTA)_. IEEE, 2021, pp. 01–07.
  * [25] T. M. Khan, S. S. Naqvi, A. Robles-Kelly, and I. Razzak, “Retinal vessel segmentation via a multi-resolution contextual network and adversarial learning,” _Neural Networks_ , 2023.
  * [26] F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” in _IEEE International Conference on 3D vision (3DV)_ , 2016, pp. 565–571.
  * [27] R. Mehta and J. Sivaswamy, “M-Net: A convolutional neural network for deep brain structure segmentation,” in _IEEE International Symposium on Biomedical Imaging (ISBI)_ , 2017, pp. 437–440.
  * [28] O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz, B. Glocker, and D. Rueckert, “Attention U-Net: Learning where to look for the pancreas,” _arXiv:1804.03999_ , 2018.
  * [29] K. C. Wong and M. Moradi, “SegNAS3D: Network architecture search with derivative-free global optimization for 3D image segmentation,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2019, pp. 393–401.
  * [30] G. Lin, A. Milan, C. Shen, and I. Reid, “RefineNet: Multi-path refinement networks for high-resolution semantic segmentation,” in _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2017, pp. 1925–1934.
  * [31] V. Nekrasov, C. Shen, and I. Reid, “Light-weight RefineNet for real-time semantic segmentation,” _arXiv:1810.03272_ , 2018.
  * [32] H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, “ICNet for real-time semantic segmentation on high-resolution images,” in _European Conference on Computer Vision (ECCV)_ , 2018, pp. 405–420.
  * [33] C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, and N. Sang, “BiSeNet V2: Bilateral network with guided aggregation for real-time semantic segmentation,” _International Journal of Computer Vision_ , vol. 129, no. 11, pp. 3051–3068, 2021.
  * [34] J. Yuan, W. Zhou, and T. Luo, “DMFNet: Deep multi-modal fusion network for RGB-D indoor scene segmentation,” _IEEE Access_ , vol. 7, pp. 169 350–169 358, 2019.
  * [35] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2017, pp. 1251–1258.
  * [36] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” _arXiv:1704.04861_ , 2017.
  * [37] A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam, “Searching for MobileNetV3,” in _IEEE/CVF International Conference on Computer Vision (CVPR)_ , 2019, pp. 1314–1324.
  * [38] Z. Zhang, C. Wu, S. Coleman, and D. Kerr, “DENSE-INception U-net for medical image segmentation,” _Computer Methods and Programs in Biomedicine_ , vol. 192, p. 105395, 2020.
  * [39] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” _IEEE Transactions Medical Imaging_ , vol. 23, no. 4, pp. 501–509, 2004.
  * [40] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” _IEEE Transactions on Biomedical Engineering_ , vol. 59, no. 9, pp. 2538–2548, 2012.
  * [41] A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” _IEEE Transactions Medical Imaging_ , vol. 19, no. 3, pp. 203–210, 2000.
  * [42] J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda, and E. Angelopoulou, “Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database,” _IET Image Processing_ , vol. 7, no. 4, pp. 373–383, 2013\.
  * [43] J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” _IEEE Transactions on Medical Imaging_ , vol. 35, no. 12, pp. 2631–2644, Dec 2016.
  * [44] P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” _PloS one_ , vol. 7, no. 3, p. e32435, 2012.
  * [45] A. Sarhan, J. Rokne, R. Alhajj, and A. Crichton, “Transfer learning through weighted loss function and group normalization for vessel segmentation from retinal images,” in _2020 25th International Conference on Pattern Recognition (ICPR)_. IEEE, 2021, pp. 9211–9218.
  * [46] P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, and F. Meriaudeau, “Indian Diabetic Retinopathy Image Dataset (IDRiD): A database for diabetic retinopathy screening research,” _Data_ , vol. 3, no. 3, p. 25, 2018.
  * [47] X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: An extremely efficient convolutional neural network for mobile devices,” in _IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2018, pp. 6848–6856.
  * [48] N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “ShuffleNet V2: Practical guidelines for efficient CNN architecture design,” in _European Conference on Computer Vision (ECCV)_ , 2018, pp. 116–131.
  * [49] S. Iqbal, S. S. Naqvi, H. A. Khan, A. Saadat, and T. M. Khan, “G-Net Light: A lightweight modified Google Net for retinal vessel segmentation,” _Photonics_ , vol. 9, no. 12, p. 923, 2022.
  * [50] F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” _Nature Methods_ , vol. 18, no. 2, pp. 203–211, 2021\.
  * [51] T. Lei, W. Zhou, Y. Zhang, R. Wang, H. Meng, and A. K. Nandi, “Lightweight V-Net for liver segmentation,” in _IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_ , 2020, pp. 1379–1383.
  * [52] T. Tarasiewicz, M. Kawulok, and J. Nalepa, “Lightweight U-Nets for brain tumor segmentation,” in _International MICCAI Brain Lesion Workshop_ , 2020, pp. 3–14.
  * [53] M. Arsalan, T. M. Khan, S. S. Naqvi, M. Nawaz, and I. Razzak, “Prompt deep light-weight vessel segmentation network (plvs-net),” _IEEE/ACM Transactions on Computational Biology and Bioinformatics_ , vol. 20, no. 2, pp. 1363–1371, 2022.
  * [54] T. M. Khan, M. Arsalan, A. Robles-Kelly, and E. Meijering, “MKIS-Net: A light-weight multi-kernel network for medical image segmentation,” _arXiv:2210.08168_ , 2022.
  * [55] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” _arXiv:2010.11929_ , 2020.
  * [56] B. Zhang, Z. Tian, Q. Tang, X. Chu, X. Wei, C. Shen, and Y. Liu, “SegViT: Semantic segmentation with plain vision transformers,” _arXiv:2210.05844_ , 2022.
  * [57] S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in _IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2021, pp. 6881–6890.
  * [58] R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Transformer for semantic segmentation,” in _IEEE/CVF International Conference on Computer Vision (ICCV)_ , 2021, pp. 7262–7272.
  * [59] R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” in _IEEE/CVF International Conference on Computer Vision (ICCV)_ , 2021, pp. 12 179–12 188.
  * [60] H. Fu, Y. Xu, S. Lin, D. W. Kee Wong, and J. Liu, “DeepVessel: Retinal vessel segmentation via deep learning and conditional random field,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2016, pp. 132–139.
  * [61] J. I. Orlando, E. Prokofyeva, and M. B. Blaschko, “A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images,” _IEEE transactions on Biomedical Engineering_ , vol. 64, no. 1, pp. 16–27, 2016.
  * [62] X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes,” _IEEE Transactions on Medical Imaging_ , vol. 37, no. 12, pp. 2663–2674, 2018.
  * [63] S. Guo, K. Wang, H. Kang, Y. Zhang, Y. Gao, and T. Li, “Bts-dsn: Deeply supervised neural network with short connections for retinal vessel segmentation,” _International Journal of Medical Informatics_ , vol. 126, pp. 105 – 113, 2019.
  * [64] Q. Jin, Z. Meng, T. D. Pham, Q. Chen, L. Wei, and R. Su, “Dunet: A deformable network for retinal vessel segmentation,” _Knowledge-Based Systems_ , vol. 178, pp. 149 – 162, 2019.
  * [65] T. Laibacher, T. Weyde, and S. Jalali, “M2U-Net: Effective and efficient retinal vessel segmentation for resource-constrained environments,” in _IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops_ , 2019, pp. 1–10.
  * [66] T. Xiang, C. Zhang, D. Liu, Y. Song, H. Huang, and W. Cai, “BiO-Net: Learning recurrent bi-directional connections for encoder-decoder architecture,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2020, pp. 74–84.
  * [67] S. Feng, Z. Zhuo, D. Pan, and Q. Tian, “CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features,” _Neurocomputing_ , vol. 392, pp. 268–276, 2020.
  * [68] K. Wang, X. Zhang, S. Huang, Q. Wang, and F. Chen, “CTF-Net: Retinal vessel segmentation via deep coarse-to-fine supervision network,” in _IEEE International Symposium on Biomedical Imaging (ISBI)_ , 2020, pp. 1237–1241.
  * [69] B. Wang, S. Wang, S. Qiu, W. Wei, H. Wang, and H. He, “CSU-Net: A context spatial u-net for accurate blood vessel segmentation in fundus images,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 25, no. 4, pp. 1128–1138, 2020.
  * [70] X. Wei, K. Yang, D. Bzdok, and Y. Li, “Orientation and context entangled network for retinal vessel segmentation,” 2022. [Online]. Available: https://arxiv.org/abs/2207.11396
  * [71] Y. Liu, J. Shen, L. Yang, H. Yu, and G. Bian, “Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images,” _Computers in Biology and Medicine_ , p. 106341, 2022.
  * [72] S. Guo, “LightEyes: A lightweight fundus segmentation network for mobile edge computing,” _Sensors_ , vol. 22, no. 9, p. 3112, 2022.
  * [73] R. Azad, M. Asadi-Aghbolaghi, M. Fathy, and S. Escalera, “Bi-directional convlstm u-net with densley connected convolutions,” in _Proceedings of the IEEE International Conference on Computer Vision Workshops_ , Oct 2019.
  * [74] D. Maji, P. Sigedar, and M. Singh, “Attention res-unet with guided decoder for semantic segmentation of brain tumors,” _Biomedical Signal Processing and Control_ , vol. 71, p. 103077, 2022.
  * [75] V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_ , vol. 39, no. 12, pp. 2481–2495, 2017.
  * [76] Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A nested u-net architecture for medical image segmentation,” in _Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI_ , 2018, pp. 3–11.
  * [77] T. M. Khan, S. S. Naqvi, M. Arsalan, M. A. Khan, H. A. Khan, and A. Haider, “Exploiting residual edge information in deep fully convolutional neural networks for retinal vessel segmentation,” in _2020 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2020, pp. 1–8.
  * [78] J. I. Orlando, E. Prokofyeva, and M. B. Blaschko, “A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images,” _IEEE Transactions on Biomedical Engineering_ , vol. 64, no. 1, pp. 16–27, 2016.
  * [79] L. Mou, Y. Zhao, L. Chen, J. Cheng, Z. Gu, H. Hao, H. Qi, Y. Zheng, A. Frangi, and J. Liu, “CS-Net: Channel and spatial attention network for curvilinear structure segmentation,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2019, pp. 721–730.
  * [80] Q. Zhang, G. Yang, and G. Zhang, “Collaborative network for super-resolution and semantic segmentation of remote sensing images,” _IEEE Transactions on Geoscience and Remote Sensing_ , vol. 60, pp. 1–12, 2021.
  * [81] Y. Hu, Z. Qiu, D. Zeng, L. Jiang, C. Lin, and J. Liu, “SuperVessel: Segmenting high-resolution vessel from low-resolution retinal image,” 2022. [Online]. Available: https://arxiv.org/abs/2207.13882
  * [82] J. H. Tan, H. Fujita, S. Sivaprasad, S. V. Bhandary, A. K. Rao, K. C. Chua, and U. R. Acharya, “Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network,” _Information Sciences_ , vol. 420, pp. 66–76, 2017.
  * [83] J. Mo, L. Zhang, and Y. Feng, “Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks,” _Neurocomputing_ , vol. 290, pp. 161–171, 2018.
  * [84] Y. Zhou, X. He, L. Huang, L. Liu, F. Zhu, S. Cui, and L. Shao, “Collaborative learning of semi-supervised segmentation and classification for medical images,” in _IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2019, pp. 2079–2088.
  * [85] S. Guo, T. Li, H. Kang, N. Li, Y. Zhang, and K. Wang, “L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images,” _Neurocomputing_ , vol. 349, pp. 52–63, 2019.
  * [86] J. Xue, S. Yan, J. Qu, F. Qi, C. Qiu, H. Zhang, M. Chen, T. Liu, D. Li, and X. Liu, “Deep membrane systems for multitask segmentation in diabetic retinopathy,” _Knowledge-Based Systems_ , vol. 183, p. 104887, 2019.
  * [87] A. Foo, W. Hsu, M. L. Lee, G. Lim, and T. Y. Wong, “Multi-task learning for diabetic retinopathy grading and lesion segmentation,” in _AAAI Conference on Artificial Intelligence_ , 2020, pp. 13 267–13 272.
  * [88] N. Sambyal, P. Saini, R. Syal, and V. Gupta, “Modified U-Net architecture for semantic segmentation of diabetic retinopathy images,” _Biocybernetics and Biomedical Engineering_ , vol. 40, no. 3, pp. 1094–1109, 2020.
  * [89] T. M. Khan, S. S. Naqvi, A. Robles-Kelly, and E. Meijering, “Neural network compression by joint sparsity promotion and redundancy reduction,” in _Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I_. Springer International Publishing Cham, 2023, pp. 612–623.
  * [90] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in _Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015_. Springer International Publishing, 2015, pp. 234–241.
  * [91] A. Howard, M. Sandler, B. Chen, W. Wang, L.-C. Chen, M. Tan, G. Chu, V. Vasudevan, Y. Zhu, R. Pang, H. Adam, and Q. Le, “Searching for mobilenetv3,” in _2019 IEEE/CVF International Conference on Computer Vision (ICCV)_ , 2019, pp. 1314–1324.
  * [92] E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 19, no. 1, pp. 263–272, 2018.
  * [93] N. Ibtehaz and M. S. Rahman, “Multiresunet : Rethinking the u-net architecture for multimodal biomedical image segmentation,” _Neural Networks_ , vol. 121, pp. 74 – 87, 2020.
  * [94] M. Arsalan, M. Oqais, tahir Mahmood, S. W. Cho, and K. R. Park, “Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation,” _Journal of Clinical Medicine_ , vol. 8, no. 9, Sep. 2019.