# InceptionCaps: A Performant Glaucoma Classification Model for Data-scarce
Environment

Gyanendar Manohar  Department of Computer Science
Munster Technological University
Cork, Ireland
0000-0001-7289-0688  Ruairi O’Reilly  Department of Computer Science
Munster Technological University
Cork, Ireland
0000-0001-7990-3461

###### Abstract

Glaucoma is an irreversible ocular disease and is the second leading cause of
visual disability worldwide. Slow vision loss and the asymptomatic nature of
the disease make its diagnosis challenging. Early detection is crucial for
preventing irreversible blindness. Ophthalmologists primarily use retinal
fundus images as a non-invasive screening method. Convolutional neural
networks (CNN) have demonstrated high accuracy in the classification of
medical images. Nevertheless, CNN’s translation-invariant nature and inability
to handle the part-whole relationship between objects make its direct
application unsuitable for glaucomatous fundus image classification, as it
requires a large number of labelled images for training. This work reviews
existing state of the art models and proposes InceptionCaps, a novel capsule
network (CapsNet) based deep learning model having pre-trained InceptionV3 as
its convolution base, for automatic glaucoma classification. InceptionCaps
achieved an accuracy of 0.956, specificity of 0.96, and AUC of 0.9556, which
surpasses several state-of-the-art deep learning model performances on the
RIM-ONE v2 dataset. The obtained result demonstrates the robustness of the
proposed deep learning model.

###### Index Terms:

Glaucoma Classification, Fundus Images, Convolutional Neural Network, Capsule
Network, Transfer Learning

##  I Introduction

Glaucoma is a leading cause of visual impairment in the world [1]. It is an
ocular disease caused by damage to the optic nerve, which sends images to the
brain. The health of the optic nerve is essential for good vision, and it can
be damaged by high intraocular pressure in the eyes, causing irreversible
vision loss. The majority of glaucoma patients are asymptomatic, with symptoms
only appearing as the disease progresses to the advanced stage. It is common
in adults in their 60s and above. Additionally, ethnic origin, genetics, and
related medical problems such as diabetes, high blood pressure, and eye
injury/surgery increase the risk of glaucoma.

Glaucoma is mainly related to the retinal and optic nerve fiber layer [2],
optic nerve assessment is essential for disease detection. It also brings
different types of abnormality in the optic disc (OD) region [3]. Hence the OD
region is the central region of interest (ROI) in digital fundus images for
glaucoma detection. The number of glaucoma patients is rising and is projected
to reach 95 million by 2030 and 11.8 million by 2040 [4]. However, due to the
complex and time-consuming testing process and a lack of expert
ophthalmologists, the world population will suffer badly from preventable
blindness. A computer-aided system for the automatic detection of glaucoma
using retinal fundus images would aid ophthalmologists in making a diagnosis,
reduce human errors and shorten the individual’s examination time.

This work is motivated to develop a performant deep learning-based neural
network for glaucoma classification that can be trained in a data-scarce
environment. Four state-of-the-art (SOTA) models, both CNN and CapsNet-based,
for glaucoma classification are reproduced. The main contribution of this work
is to demonstrate the utility of transfer learning (TL) over data augmentation
in limited training dataset scenarios. Consequently InceptionCaps, a novel
deep learning (DL) model consisting of a pre-trained InceptionV3 and capsule
network for glaucoma classification is proposed.

Artificial neural networks (ANN) have gained notable success in classification
and object detection with advancements in computer vision and machine learning
(ML). Over the past decade, extensive research has been conducted for
computer-aided detection of glaucoma from retinal fundus images using imagery
transformation, supervised machine learning, semi-supervised machine learning,
and DL techniques.

CNN are a type of deep-feed forward artificial neural network used across the
domain for image classification. The main benefit of CNN architectures is
their ability to extract highly discriminating features at multiple levels of
abstraction. The main challenges are as follow:

  1. i)

CNNs can predict an object’s presence in an image but have no knowledge about
the position of one object with respect to another. So, when an object is
moved from its position, the CNN cannot detect this movement. This is the
translation-invariant property of CNN.

  2. ii)

CNNs requires a large training dataset. Data is required from all viewpoints
to generalize the result. The pooling layer of CNN discards lots of features
value from the feature map and requires significant quantities of training
data to compensate for the loss. The large training dataset requirement makes
the training of CNN slow and resource-intensive.

Retinal fundus imagery has many differentiating features present in the lower-
level pixel. CNN’s pooling layer discarding part of them makes it difficult
for a model to train on a small dataset. The ethical and data protection
issues associated with patient fundus imagery makes it challenging to acquire
data in sufficient quantities for training CNNs. Table I denotes publicly
available glaucomatous fundus image datasets used for the automatic
classification of glaucoma. The main challenges with the referenced datasets
are as follows:

  1. 1.

Datasets have fewer training images, which is unsuitable for training a CNN-
based neural network.

  2. 2.

Images in the dataset are not diverse. For example, it does not include images
taken from different camera models and patients from different ethnicities.

  3. 3.

Datasets have a significant class imbalance issue.

CapsNet [7] overcame some of the shortcomings of CNNs. Its main building
blocks are capsule, a small group of neurons where each neuron represents the
various properties of a particular part of the image and outputs a vector. The
magnitude of the vector determines the likelihood of the presence of a
particular object or region in the image represented by the capsule.
Furthermore, the capsule network strives for viewpoint equivariance, i.e., any
change in viewpoint at input results in a similar variance in the output’s
viewpoint. If an image is divided into different parts, each part will have
its characteristic, which a set of characteristic features can represent. A
neuron can represent a specific property of an object or region within the
picture. So, a group of neurons can represent several properties of the
object. CapsNet requires relatively fewer images for training [8] and performs
robustly with class imbalance issues [9], which makes it an appealing choice
for medical image classification.

TABLE I: Public Retinal fundus image dataset for glaucoma classification

Dataset Ref | No. Images | Glau | Normal | Task
---|---|---|---|---
RIM-ONE v1 [10] | 131 | 39 | 92 | CLS,SEG
RIM-ONE v2 [10] | 455 | 200 | 255 | CLS
RIM-ONE v3 [10] | 159 | 74 | 85 | CLS,SEG
RIM-ONE dl [11] | 485 | 172 | 313 | CLS
DRISHTI-GS [12] | 101 | 70 | 31 | CLS
ACRIMA [20] | 705 | 396 | 309 | CLS
ODIR [21] | 5000 | 207 | 1135 | CLS
G1020 [22] | 1020 | 296 | 724 | CLS,SEG

*CLS is Classification *SEG is Segmentation *Glau is Glaucomatous *No.Images is Number of Images

Transfer learning (TL) and data augmentation are widely used due to data
scarcity in the medical domain. Various data augmentation strategies have been
successfully applied in the medical domain to force the model to learn
discriminative image features and have improved model performance. Image
transformations made to an image during augmentation may cause the generated
image to lose its discriminative features, degrading model performance. The
suitability of a combination of augmentation techniques for different medical
images is under-researched.

DL-based generative adversarial networks (GAN) have also been used to produce
synthetic retinal images for training CNN models for eye diseases
classification [26]. However, GAN-based models are complex and require high-
end computational resources, which can limit their utility [28]. Hence, this
work focuses on TL, and DL model architecture for glaucoma classification from
fundus images.

The reminder of the paper is organised as follows: Section II provides an
overview of related work in glaucoma classification from fundus imagery using
CapsNet and TL-based DL model. Section III details the dataset selection,
design of the baseline DL model, and CapsNet. Section IV presents results and
discusses the findings. Section V details the conclusion of this work and
outlines suggested future work.

##  II Related Work

A significant body of research exists on the automatic classification of
retinal diseases, including glaucoma, from the fundus imagery using DL. The
automated feature extraction capabilities of DL make it a preferred choice for
fundus image segmentation and classification. CNNs are widely utilised for
this image classification task. However, it is difficult to train CNNs from
scratch for glaucoma classifications due to the need for large training
datasets, which are scarce. Consequently, researchers have used TL and
multiple data augmentation methods when training glaucoma classification
models.

TABLE II: DL based glaucoma classification model with their architecture,
dataset used, augmentation techniques, Optimiser, performance metrics and
limitation

Model Ref |  Architecture |  TLA |  Dataset |  Aug |  Opt |  AUC |  SEN |  SPE |  ACC |  Re-Imp |  Limitations
---|---|---|---|---|---|---|---|---|---|---|---
Bander [18] |  AlexNet, SVM |  FE |  R1 v1 |  - |  - |  - |  0.85 |  0.908 |  0.882 |  ✓ |  -
Cerentini [19] |  GoogLeNet |  FT |  R1 v1, v2, v3 |  Rot, re-scaling, Gv, Gna |  ADAM |  0.862 |  - |  - |  - |  ✓ |  -
Batista [11] |  V16, V19, RNet, DNet, MobileNet, InV3 |  FT |  R1 dl |  Hvf,Rot, zoom |  RMSProp |  |  |  |  |  ✓ |  -
Maheshwari [23] |  AlexNet |  FT |  R1 v2 |  Lbp |  SGDM |  - |  1 |  0.975 |  0.989 |  |  Custom prep
Sallam [24] |  Pre-trained CNN |  FT |  LAG |  - |  |  0.869 |  0.869 |  - |  - |  |  Private dataset
Lima [25] |  ResNet, |  FE |  R1 v2 |  - |  |  0.957 |  - |  - |  0.901 |  |  Custom prep
|  INet, LR |  |  R1 v3 |  |  |  0.860 |  - |  - |  0.805 |  |
Phankokkruad [27] |  V16, RNetV2, Xpt, InV3 |  FT |  REFUGE |  - |  |  0.9719 |  - |  - |  - |  |  Dataset unavailable
Capsule Network based models for glaucoma classification |  |  |
dos Santos [14] |  CapsNet |  - |  R1 v2 |  - |  RMSProp |  - |  - |  - |  0.909 |  ✓ |
Gaddipatis [13] |  CapsNet |  - |  - |  - |  - |  - |  - |  - |  |  |  3D OCT Images

*TLA is Transfer Learning Approach *Aug is Augmentation *Opt is Optimiser *AUC is Area Under the ROC Curve *SEN is sensitivity *SPE is Specificity *ACC is accuracy *Re-Imp is Re-Implemented *Prep is preprocessing *FE is Feature Extraction *FT is Fine Tuning *R1 is RIM-ONE *Rot is Random rotation *Lbp is Local binary pattern *Gv is Gamma variation *Gna is Gaussian noise addition *Hvf is Horizontal and vertical flip *V is VGG *RNet is ResNet50 *DNet is DenseNet121 *Xpt is Xception *InV3 is InceptionV3 *INet is InceptionNet

###  II-A Transfer Learning

A TL-based approach reduces model training time and can improve classification
performance. There are two approaches to TL, namely feature extraction and
fine-tuning, both of which have been used for developing glaucoma
classification models (See Table II).

Feature extraction: CNNs have excellent feature extraction capability. Pre-
trained CNNs extract features from the training input image, which acts as
input to the linear classifiers like RF, LR, and SVM to do the actual
classification [18]. Fine tuning: The pre-trained CNNs contain many layers.
The initial layers learn low-level features of the image, whereas higher
layers learn the more significant structures. For using it with a different
dataset, the pre-trained network’s top layer is removed, and a new fully
connected layer is added. The resulting network is trained on a new dataset
after freezing the weight of the pre-trained network [11] [19].

###  II-B Capsule Network

As denoted in Table II, CapsNet have been used for retinal disease
classification. CapsNet [7], when originally proposed, used routing algorithms
for information propagation across the layer. The routing process is
computationally expensive and consequently slows down the training process.
Furthermore, using the same size convolution kernel for feature generation is
insufficient to capture sufficient feature depth from an image where the
target objects have complex representation [15]. Several improvements, such as
multi-scale convolution layer [16] and modified routing algorithm [17], have
been introduced to overcome these limitations.

##  III Methodology

Four SOTA models, are re-implemented as indicated in Table II. Each SOTA model
undergoes more extensive experimentation in order to evaluate the models’
performance. The following stages of a DL workflow for classifying
glaucomatous fundus images are detailed: i) Data selection, ii) Data
preprocessing, iii) Model preparation/selection, iv) Training, validating, and
tuning the model, and v) Class prediction for test data.

  * i)

Dataset selection: Retinal Image database for Optic Nerve Evaluation (RIM-ONE)
[10] is a highly cited publicly available dataset. RIM-ONE contains four
versions v1, v2, v3, and dl. RIM-ONE v2 and RIM-ONE dl [11] contain fundus
images segmented by experts. ACRIMA [20] is a glaucoma labeled public dataset
from the ACRIMA project. This dataset is well suited for classification as
images are segmented around the optic disc region.

  * ii)

Pre-processing: All images are resized as per selected pre-trained CNN model
input requirement, and their pixels’ values are normalized in the range of
[0,1].

  * iii)

Model Selection: Results of all SOTA models could not be faithfully reproduced
due to limitations such as the use of a private dataset, custom preprocessing
steps, or omission of details relating to model architecture. As denoted in
Table II, TL-based models that were trained on publicly available data,
without any specialized preprocessing, were selected for the purposes of this
work.

  * iv)

Training and Validation: The training and validation methods used are adapted
from their descriptions. Most hyper-parameter values used were taken from the
SOTA model selected for reproduction, and further experimentation is
highlighted in the relevant section. Missing hyperparameter values were
empirically determined by observing the model performance. It is observed that
during the training of a model, the epoch point where validation loss is
minimum does not match with point where validation accuracy is maximum. The
difference is in the range of 1-2%. As validation loss is the more stable
parameter for comparing the performance, this work utilises the model
checkpointing method to select the model where validation loss is minimum.

  * v)

Evaluation Metrics: The selection of evaluation metrics is important as model
performance and their interpretation can vary. Accuracy is the most commonly
used evaluation metric for glaucoma classification, followed by Area Under the
Receiver Operating Characteristics Curve (AUC), precision, and recall.

  * Note: All works used Tensor flow and Keras, OpenCV, Matplotlib and Matlab live script for the model implementation on Google Collaborator with high-speed RAM.

###  III-A DL model details and their reproduction steps

####  III-A1 AlexNet based model

Al-Bander [18] model was re-implemented using MATLAB as Keras library does not
provide pre-trained AlexNet.

####  III-A2 GoogLeNet based model

For reproducing Cerentini [19] model, the top layer of an Imagenet trained
InceptionV3 is removed, and a new fully-connected layer with two class output
softmax added. The pre-trained weights of InceptionV3 are kept frozen and the
model is trained with a batch size of 32, and a learning rate of 0.0001. The
validation accuracy is monitored with a patience value of 10.

####  III-A3 Transfer learning based RIM-ONE DL validation

The model detailed in RIM-ONE dl’s original paper [11] is implemented. Random
rotation of (-30,30) degrees, vertical and horizontal flips, and zoom ranges
0.8 to 1.2 are applied to the input image for data augmentation. The pre-
trained models VGG16, VGG19, ResNet50, DenseNet121, and MobileNetV2 trained on
the Imagenet dataset for TL are used.

The fully connected layers of the pre-trained model are replaced with new
fully connected layers for the two-class output softmax layer. In addition,
the GlobalAveragePooling2D layer was added to the convolutional base. The
resultant model is trained with a batch size of 32. The model is trained and
fine-tuned by firstly, freezing the pre-trained layer’s weight and training
the newly added top layer with the learning rate 2e-5, secondly, unfreezing
the pre-trained layers and jointly training the complete network with the
learning rate 1e-5. The training uses 5-fold cross-validation by keeping a
.8:.2 ratio of training data for the training and validation set.

The resultant model is trained again on a full training dataset using the
steps used previously with cross-validation. Finally, the resultant model
classification performance is evaluated with the test dataset.

The RIM-ONE dl dataset has a class imbalance issue, so accuracy is not chosen
as the primary metric for measuring the model performance. This is to avoid
the biases exhibited by the model. Instead, AUC-ROC was used as a reference
for evaluating network performance. This metrics is complemented with
sensitivity and specificity.

####  III-A4 Capsule network based model

The CapsNet-based model in [10] was selected for reproduction and establishing
baseline performance for glaucoma classification. The original work used RIM-
ONE v2 [10] and Drishti-GS [12] dataset for model performance evaluation.

CapsNet tries to model every part of the image, which increases the
computational complexity, slows down the training, and reduces the accuracy.
As part of preprocessing, images are resized to 64$\times$64 pixels and,
histogram equalization (HE) is applied to each image to enhance the contrast,
structural and visual aspects before passing to the network. This makes the
model robust against the sharp variation in the image contrast, which can
impact the image features learned by the network.

The model has two convolutional layers followed by a fully connected layer.
The first layer has 256 filters with a 9$\times$9 kernel with stride value one
and ReLu activation function. The second layer is the primary capsule layer,
having eight-dimensional 32 convolutional capsules with 9$\times$9 cores and a
stride value of 2. The third layer is the digitcap layer, which produces
16-dimensional vector output for each class. This work focuses on the full-
color space only. The final model is trained for 200 epochs with a learning
rate of 0.0001.

###  III-B Experimental Analysis

In order to evaluate the SOTA DL models presented in Section III-A, a
comparative analysis is performed. In this context, each model will adopt data
preprocessing, augmentation, TL, and classification as reported originally in
the first instance. The following experiments are to be carried out for each
model:

  1. 1.

Evaluate model performance on RIM-ONE v2, RIM-ONE dl, and ACRIMA dataset.

  2. 2.

Study the model performance with and without data augmentation.

  3. 3.

Evaluate model performance for different test train split ratios.

  4. 4.

Solidify the usability of TL techniques under limited training data
availability by using different CNNs.

  5. 5.

Evaluate model generalisation capability across different datasets.

For the CapsNet model, the first layer is a convolutional layer that generates
the feature maps from the input image. It has been discovered during the
related work review in Section II-B that a single convolutional layer may not
always be able to extract sufficient features to get decent performance. This
makes it imperative to study the CaspNet classification performance with
changes in the initial convolutional layer. For the CapsNet, the following
experimentation is undertaken:

  1. 1.

Change filter count and the kernel size of the convolutional layer of the
discussed CapsNet architecture in the previous section.

  2. 2.

Use the TL approach with CapsNet and study the impact on classification
performance of the resulting model.

The intent is to utilize the automatic feature extraction power of pre-trained
CNNs whose top layers are replaced with the primary capsule and the class
capsule with two outputs. The pre-trained part of the model extracts high-
level feature details, and the capsule part extracts low-level features from
the fundus image. The resulting model is trained end to end for glaucoma
classification.

##  IV Results and Discussion

This section presents a quantitative evaluation of model performance for
glaucoma classification. Each model’s performance is first compared to the
stated result in the model’s original research article. Then, the outcome of
all of the model’s experimentation as described in Section III-B is presented
and discussed with the underlying model to maintain flow coherency.

###  IV-A AlexNet based model MIV-A

The AlexNet-based CNN described in Section III-A1 is hereafter referred to as
MIV-A. Table III denotes the classification performance of MIV-A (AlexNet,
SVM) and the alternate pre-trained CNN, classifier permutations. MIV-A’s
performance is comparable to that reported in [18], confirming the model’s
reproducibility. As MIV-A performed well on the RIM-ONE v2 dataset without any
additional preprocessing or data augmentation, it is important to investigate
the model’s classification performance across a variety of datasets, the
impact of data augmentation, the efficacy of various models, and the utility
of a TL-based model in a limited training dataset scenario.

TABLE III: Glaucoma classification performance for different architecture,
train test split ratio across different datasets

Model |  Dataset |  Tr:Te | Aug |  ACC |  SEN |  SPE
---|---|---|---|---|---|---
Bander [18] |  R1 v2 |  0.7:0.3 | No |  0.882 |  0.85 |  0.908
MIV-A |  R1 v2 |  0.7:0.3 | No |  0.889 |  0.883 |  0.894
MIV-A |  R1 dl |  0.7:0.3 | No |  0.890 |  0.865 |  0.904
MIV-A |  ACRIMA |  0.7:0.3 | No |  0.943 |  0.966 |  0.914
MIV-A |  R1 v2 |  0.6:0.4 | No |  0.879 |  0.875 |  0.882
MIV-A |  R1 v2 |  0.8:0.2 | No |  0.901 |  0.850 |  0.941
MIV-A |  R1 v2 |  0.8:0.2 | Rot,T |  0.901 |  0.850 |  0.941
MIV-A |  R1 v2 |  0.8:0.2 | T,Ref |  0.901 |  0.850 |  0.941
MIV-A |  R1 v2 |  0.8:0.2 | T |  0.901 |  0.850 |  0.941
MV2, LR |  R1 v2 |  0.8:0.2 | No |  0.934 |  0.931 |  0.936
MV2, SGD |  R1 v2 |  0.8:0.2 | No |  0.905 |  0.948 |  0.873
MV2, SVM |  R1 v2 |  0.8:0.2 | No |  0.875 |  0.862 |  0.886
ResNet50, LR |  R1 v2 |  0.8:0.2 | No |  0.890 |  0.827 |  0.936
ResNet50, RF |  R1 v2 |  0.8:0.2 | No |  0.875 |  0.793 |  0.936
VGG19, LR |  R1 v2 |  0.8:0.2 | No |  0.883 |  0.793 |  0.949
VGG16, LR |  R1 v2 |  0.8:0.2 | No |  0.875 |  0.810 |  0.924
DN121, SGD |  R1 v2 |  0.8:0.2 | No |  0.868 |  0.844 |  0.886
DN121, SVM |  R1 v2 |  0.8:0.2 | No |  0.861 |  0.775 |  0.924
DN121, LR |  R1 v2 |  0.8:0.2 | No |  0.861 |  0.793 |  0.911
InceptionV3, LR |  R1 v2 |  0.8:0.2 | No |  0.817 |  0.758 |  0.860
InceptionV3, SGD |  R1 v2 |  0.8:0.2 | No |  0.810 |  0.689 |  0.898
InceptionV3, SVM |  R1 v2 |  0.8:0.2 | No |  0.817 |  0.689 |  0.911

*R1 is RIM-ONE *Tr:Te is Train:Test *Aug is Augmentation *Rot is Random Rotation *T is Translation *Ref is Reflection *ACC is Accuracy *SEN is Sensitivity *SPE is Specificity *MV2 is MobileNetV2 *DN121 is DenseNet121

####  IV-A1 Performance across different datasets

MIV-A’s classification performance is nearly identical for RIM-ONE dl and RIM-
ONE v2 but achieved higher accuracy for the ACRIMA dataset, which has more
images, and the image quality is optimized for deep learning.

####  IV-A2 Varying train test split ratio

For the train test ratio of 0.8:0.2, MIV-A achieved the highest specificity,
which directly measures the true negative value. With the increase in ratio,
the model had more training examples, which increased the model’s sensitivity
and reduced specificity.

####  IV-A3 Effect of data augmentation

MIV-A classification performance improved by 1%, which is not a considerable
improvement given TL alone achieved an accuracy of around 88%. With
translation alone, MIV-A classification performance didn’t improve, whereas
with random rotation, the model classification performance degraded. Hence, TL
is considered a better approach to augmentation for training the CNN in a
limited data environment.

####  IV-A4 Pre-trained CNN and classifier permutations

In Table III, the combination of MobileNetV2 with LR was more performant than
other pre-trained CNNs’ on the RIM-ONE v2 dataset. The MobileNetV2 based CNN
is hereafter referred to as MIV-A4.

####  IV-A5 Model generalisation on different dataset

The number of images in RIM-ONE v2 and RIM-ONE dl are limited and do not
include images from different ethnicities, different camera models, or the
rare cases of glaucoma. MIV-A does not learn sufficient unique patterns to
generalize well for different fundus image datasets. The result indicates that
the model cannot generalize well on diverse test input and should be trained
on a larger, more diverse dataset to enable more generalised performance.

###  IV-B GoogLeNet based model MIV-B

The GoogleNet based CNN described in Section III-A2 is hereafter referred to
as MIV-B. Table IV denotes the classification performance of MIV-B. MIV-B’s
performance is comparable to that reported in [19], confirming the model’s
reproducibility.

TABLE IV: Glaucoma classification performance with different train test split
ration, architecture across datasets

Model |  Architecture |  Dataset |  Tr:Te | Aug | ACC
---|---|---|---|---|---
[19] |  GoogleNet |  R1 v2 |  0.9:0.1 | No | 0.862
MIV-B |  GoogleNet |  R1 v2 |  0.9:0.1 | No | 0.869
MIV-B |  GoogleNet |  R1 dl |  0.9:0.1 | No | 0.869
MIV-B |  GoogleNet |  ACRIMA |  0.9:0.1 | No | 0.901
MIV-B |  GoogleNet |  R1 v2 |  0.7:0.3 | No | 0.897
MIV-B |  GoogleNet |  R1 v2 |  0.9:0.1 | Yes | 0.913
MIV-B |  GoogleNet |  R1 v2 |  0.8:0.2 | No | 0.912
MIV-B |  GoogleNet |  R1 v2 |  0.7:0.3 | No | 0.897
MIV-B |  InceptionV3 |  R1 v2 |  0.9:0.1 | No | 0.901
MIV-B |  MobileNet |  R1 v2 |  0.9:0.1 | No | 0.890
MIV-B |  DenseNet121 |  R1 v2 |  0.9:0.1 | No | 0.890
MIV-B |  MobileNetV2 |  R1 v2 |  0.9:0.1 | No | 0.879
MIV-B |  Xception |  R1 v2 |  0.9:0.1 | No | 0.868
MIV-B |  VGG16 |  R1 v2 |  0.9:0.1 | No | 0.835
MIV-B |  IRV2 |  R1 v2 |  0.9:0.1 | No | 0.824
MIV-B |  VGG19 |  R1 v2 |  0.9:0.1 | No | 0.802
MIV-B |  ResNet50 |  R1 v2 |  0.9:0.1 | No | 0.681

*Tr:Te is Train:Test *Aug is Augmentation *R1 is RIM-ONE *ACC is Accuracy *IRV2 is InceptionResNetV2

####  IV-B1 Performance across different datasets

MIV-B’s classification performance is nearly identical for RIM-ONE dl and RIM-
ONE v2 but achieved higher accuracy for the ACRIMA dataset, which has more
images, and the images quality is optimized for the deep learning model.

####  IV-B2 Varying train test split ratio

MIV-B classification performance improved as the number of training images
increased. This is because the model learns more fundus image attributes as
more training data is provided.

####  IV-B3 Effect of data augmentation

MIV-B classification accuracy improved by 4%, when the horizontal and vertical
flip, random rotation of 10 degrees, and zooming were applied during the model
fine-tuning.

####  IV-B4 Pre-trained CNNs

Models’ performance varied by changing the pre-trained CNN. The resultant
network consisting of InceptionV3 is more performant than other pre-trained
CNNs. The InceptionV3-based CNN is hereafter referred to as MIV-B4.

####  IV-B5 Model generalisation on different datasets

RIM-ONE dl and RIM-ONE v2 datasets have images from the same source, so model
performance is almost at the same level. However, the images of the ACRIMA
dataset have a different resolution, and the source and model trained on the
RIM-ONE dataset do not perform well. This indicates that MIV-B cannot
generalize well on different datasets unless trained on larger dataset with
diverse images.

TABLE V:  MIV-A and MIV-B generalisation capability across the datasets Model | Training Dataset | Test Dataset | Accuracy
---|---|---|---
MIV-A | RIM-ONE v2 | RIM-ONE dl | 0.828
|  | ACRIMA | 0.495
MIV-A | RIM-ONE dl | RIM-ONE v2 | 0.904
|  | ACRIMA | 0.575
MIV-B | RIM-ONE v2 | RIM-ONE dl | 0.178
|  | ACRIMA | 0.436
MIV-B | RIM-ONE dl | RIM-ONE v2 | 0.195
|  | ACRIMA | 0.521

###  IV-C Transfer learning based RIM-ONE DL validation MIV-C

Different CNN-based models are developed as described in Section III-A3 to
validate the reported performance for glaucoma classification on the RIM-ONE
dl dataset. Table VI denotes the glaucoma classification performance metrics
reported in [11] and the reproduced metrics using the steps mentioned in
Section III-A3.

The TL-based RIM-ONE DL validation is hereafter referred to as MIV-C. Data
augmentation techniques random rotation (-30,30) degree, zoom, vertical, and
horizontal flip are used to avoid overfitting during the model training.

MIV-C’s glaucoma classification performance is approximately 0.07 points less
than the reported AUC value in [11] for most pre-trained networks. The
difference in outcome could be because of the implementation mismatch.
Unfortunately, the complete details about the network architecture used are
omitted in [11].

TABLE VI: MIV-C glaucoma classification performance on RIM-ONE dl dataset with
and without augmentation

| Reported | Achieved - Aug | Without Aug
---|---|---|---
P Model |  AUC |  SEN |  ACC |  AUC |  SEN |  ACC |  AUC |  SEN |  ACC
VGG16 |  0.983 |  0.961 |  0.924 |  0.917 |  0.989 |  0.938 |  0.897 |  0.957 |  0.872
VGG19 |  0.986 |  1.000 |  0.931 |  0.920 |  0.936 |  0.924 |  0.876 |  0.872 |  0.878
Xception |  0.977 |  0.980 |  0.917 |  0.901 |  0.957 |  0.917 |  0.890 |  0.936 |  0.871
ResNet50 |  0.975 |  0.980 |  0.911 |  0.500 |  1.000 |  0.643 |  0.643 |  1.000 |  0.500
MNetV2 |  0.973 |  0.942 |  0.904 |  0.712 |  0.925 |  0.773 |  0.760 |  0.861 |  0.719
DenseNet |  0.972 |  0.961 |  0.904 |  0.938 |  0.914 |  0.931 |  0.890 |  0.904 |  0.884
MNet |  0.971 |  0.961 |  0.931 |  0.939 |  0.936 |  0.938 |  0.890 |  0.904 |  0.884
IRV2 |  0.968 |  0.980 |  0.911 |  0.895 |  0.925 |  0.904 |  0.842 |  0.872 |  0.830
IV3 |  0.959 |  0.942 |  0.890 |  0.863 |  0.957 |  0.890 |  0.849 |  0.840 |  0.852

*AUC is areas under ROC curve *SEN is sensitivity *ACC is accuracy *Aug is Augmentation *MNet is MobileNet *IRV2 is InceptionResNetV2 *IV3 is InceptionV3 *P Model is Pre-trained Model

####  IV-C1 Impact of data augmentation

MIV-C’s classification performance improved by an average of 5% with data
augmentation.

####  IV-C2 Performance across different datasets

The model has performed well for the ACRIMA dataset.

MIV-C2 The Xception-based model has achieved the highest AUC value of 0.921.
Xception based model is hereafter referred to as MIV-C2.

TABLE VII: MIV-C glaucoma classification performance on RIM-ONE v2 and ACRIMA
dataset without augmentation

| ACRIMA | RIM-ONE v2
---|---|---
Pre-trained Model | AUC | SEN | ACC | AUC | SEN | ACC
vgg16 | 0.973 | 0.982 | 0.971 | 0.906 | 0.853 | 0.912
vgg19 | 0.950 | 0.948 | 0.950 | 0.899 | 0.978 | 0.901
ResNet50 | 0.733 | 0.551 | 0.765 | 0.500 | 1.000 | 0.450
DenseNet121 | 1.00 | 1.00 | 1.00 | 0.869 | 0.878 | 0.868
MobileNet | 0.979 | 0.982 | 0.978 | 0.913 | 0.926 | 0.912
MobileNetV2 | 0.586 | 0.172 | 0.659 | 0.704 | 0.609 | 0.714
InceptionV3 | 0.985 | 0.982 | 0.985 | 0.864 | 0.829 | 0.868
IRV2 | 0.973 | 0.982 | 0.971 | 0.884 | 0.829 | 0.890
xception | 0.973 | 0.982 | 0.971 | 0.921 | 0.902 | 0.923

*ACC is Accuracy *AUC is Area Under the ROC Curve *SEN is Sensitivity *IRV2 is InceptionResNetV2

Glaucoma classification performance of MIV-A, MIV-B, and MIV-C confirms that
TL techniques are helpful for a limited training example scenario.

###  IV-D Capsule network-based model MIV-D

Table VIII denotes the achieved classification performance of CapsNet
discussed in Section III-A4. MIV-D performance for glaucoma classification is
comparable to that reported in [14] for all the evaluation metrics values,
confirming the model’s reproduciblity. The images from the Drishti-GS1 dataset
are full fundus images, and no segmentation was performed on the images before
being passed to the network during training. The model performed better than
MIV-A and MIV-B, even with full fundus images and without data augmentation.
The HE has enhanced the image characteristics, enabling improved learning and,
hence, improved classification accuracy.

TABLE VIII: MIV-D glaucoma classification performance across different dataset
with different image size and convolution layer

HE |  Model |  Dataset |  Img Size |  Con Layer |  ACC |  AUC
---|---|---|---|---|---|---
Yes |  [14] |  R1 v2, GS1 |  64$\times$64 |  256(9$\times$9) |  0.909 |  0.904
Yes |  MIV-D |  R1 v2, GS1 |  64$\times$64 |  256(9$\times$9) |  0.892 |  0.890
No |  [14] |  R1 v2, GS1 |  64$\times$64 |  256(9$\times$9) |  0.882 |  0.891
No |  MIV-D |  R1 v2, GS1 |  64*64 |  256(9$\times$9) |  0.875 |  0.873
Yes |  MIV-D |  R1 v2 |  64$\times$64 |  256(9$\times$9) |  0.912 |  0.913
No |  MIV-D |  R1 v2 |  64$\times$64 |  256(9$\times$9) |  0.879 |  0.876
Yes |  MIV-D |  R1 v2 |  128$\times$128 |  256(9$\times$9) |  0.857 |  0.852
Yes |  MIV-D |  R1 dl |  64$\times$64 |  256(9$\times$9) |  0.904 |  0.873
No |  MIV-D |  R1 dl |  64$\times$64 |  256(9$\times$9) |  0.876 |  0.861
Yes |  MIV-D |  ACRIMA |  64$\times$64 |  256(9$\times$9) |  0.893 |  0.894
No |  MIV-D |  ACRIMA |  64$\times$64 |  256(9$\times$9) |  0.964 |  0.964
No |  MIV-D3 |  R1 v2 |  64$\times$64 |  128(9$\times$9) |  0.901 |  0.901
No |  MIV-D3 |  R1 v2 |  64$\times$64 |  64(9$\times$9) |  0.890 |  0.889
No |  MIV-D3 |  R1 v2 |  64$\times$64 |  64(7$\times$7) |  0.824 |  0.824
No |  MIV-D3 |  R1 v2 |  64$\times$64 |  128(9$\times$9), 64(9$\times$9) |  0.891 |  0.901
No |  MIV-D3 |  R1 v2 |  64$\times$64 |  32(3$\times$3), 64(5$\times$5), 128(7$\times$7) |  0.912 |  0.909
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  InceptionV3 |  0.956 |  0.955
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  MobileNet |  0.945 |  0.943
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  DenseNet121 |  0.912 |  0.904
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  MobileNetV2 |  0.802 |  0.791
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  Xception |  0.934 |  0.929
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  VGG16 |  0.934 |  0.931
No |  MIV-D4 |  R1 v2 |  64$\times$64 |  VGG19 |  0.879 |  0.876

*HE is Histogram equalisation *ACC is Accuracy *AUC is Area Under the ROC Curve *R1 is RIM-ONE *GS1 is DRISHTI-GS1 *Con is Convolutional

####  IV-D1 Performance for different image sizes

MIV-D’s classification performance degraded with large image size, and the
reason discussed in Section III-A4. Therefore, for all further
experimentation, unless stated otherwise, the images are resized to
64$\times$64 pixels.

####  IV-D2 Performance across different datasets

MIV-D glaucoma classification performance for ACRIMA, RIM-ONE dl and RIM-ONE
v2 dataset is better than many of the TL-based models discussed in previous
sections for the RIM-ONE v2 and the RIM-ONE dl dataset. Also, HE shouldn’t be
applied to all types of images as evident from the degradation in model
performance for the ACRIMA dataset.

####  IV-D3 Impact of different kernels and filters

The CapsNet with different filter counts and kernels is hereafter referred to
as MIV-D3. Table VIII denotes MIV-D3 glaucoma classification performance on
RIM-ONE v2 dataset. The performance of the CapsNet model varies with the
change in filter count and kernel size of the base convolutional layer. A
large filter count with a larger kernel size produces a rich feature map,
which enhances the models’ classification performance.

The main takeaway of Section IV-D3 is that a single convolutional layer with a
single kernel size cannot extract sufficient image features. However, a deeper
convolutional base increases the training data requirement, which defies the
main advantage of capsule network usability with a limited training set. These
experiments provided an intuition of InceptionCaps, described in the next
section.

####  IV-D4 Pre-trained CNN and capsule network

Pre-trained CNNs are good feature extractors, as indicated by the performance
of MIV-A, and MIV-B. The CNN outputs the spatial representation of the input
image instead of the single convolutional layer, as discussed in Section
III-A4. The CapsNet with CNN as the base is hereafter referred to as MIV-D4.
The model performance is evaluated on the RIM-ONE v2 dataset. Resizing and
pixel value rescaling were applied to the input image.

####  IV-D5 InceptionCaps

The most performant model from the experimental analysis utilised InceptionV3
as the base layer of the CapsNet (See Table IX). It achieved an accuracy of
0.956 and AUC of 0.9556 on the RIM-ONE v2 dataset. Additionally, this
outperforms all prior results presented from Section IV-A to Section IV-C as
well as the original SOTA models selected for re-implementation from Table II.

In isolation, the convolutional layer would discard low-level features from
the input fundus image, critical for glaucoma classification. InceptionCaps
utilizes low-level feature representation of CapsNet and feature extraction of
CNNs. The boost in the classification performance is due to this unique
characteristic of the capsule network.

InceptionV3 was selected as the base of the CapsNet architecture. MobileNet
achieved a comparable accuracy (see Table VIII). The primary difference
between these two models is that MobileNet uses depthwise separable
convolution, which reduces the number of learnable parameter requirements and
slightly degrades performance compared to InceptionV3, which uses standard
convolution.

TABLE IX: Glaucoma classification performance across DL model implemented and
SOTA model on RIM-ONE v2 dataset

Model |  Architecture |  ACC |  AUC |  SEN |  SPE
---|---|---|---|---|---
[18] |  TL - AlexNet |  0.882 |  |  0.85 |  0.908
[19] |  TL - GoogLeNet |  0.86 |  - |  - |  -
[23] |  TL - AlexNet |  0.989 |  - |  - |  -
[25] |  TL |  0.901 |  0.957 |  - |  -
MIV-A4 |  TL - MobileNetV2 |  0.934 |  0.933 |  0.931 |  0.936
MIV-B4 |  TL - InceptionV3 |  0.901 |  0.896 |  0.853 |  0.94
MIV-C2 |  TL - Xception |  0.921 |  0.902 |  0.923 |
InceptionCaps |  TL - CapsNet |  0.956 |  0.955 |  0.951 |  0.96

*TL is Transfer learning *ACC is Accuracy *SEN is Sensitivity *SPE is Specificity

###  IV-E Final Discussion

Table IX denotes the glaucoma classification performance on the RIM-ONE v2
dataset of the novel DL model proposed in this work (InceptionCaps),
reproduced SOTA models (MIV-A, MIV-B, MIV-C, MIV-D) and SOTA models that could
not be reproduced [23, 25] (due to the omission of custom image preprocessing
techniques and details relating to the model implementation).

InceptionCaps achieved the highest accuracy in all but one instance, in the
context of TL-based CNNs MIV-A, MIV-B and MIV-C. The ability to retain low-
level features enabled by the CapsNet assisted with the performance gain.
CapsNet works well for smaller image sizes, and the performance starts to
degrade as the images size increases (see Table VIII).

CapsNet with a CNN base achieved classification accuracy of 0.956, which
outperformed [11],[18] and [19] for glaucoma classification from the fundus
images. InceptionCaps obtained the highest specificity of 0.96, indicating
that most glaucomatous eyes were detected as glaucomatous. In addition,
InceptionCaps achieved the highest accuracy without any additional
preprocessing and data augmentation.

Compared to SOTA CNNs’ reporting performance on the RIM-ONE v2 dataset, only
the model in [23] performed better than InceptionCaps. In [23] the training
image count is augmented by treating each channel of input RGB image as an
unique instance, and an LBP transformation is applied to it. In contrast, no
direct or indirect data augmentation was applied during the training of
InceptionCaps.

As seen in Section IV-A3, IV-B3 and IV-C1, geometric transformation-based data
augmentation techniques enhance the model learning capability and improves
classification performance. Images generated post-augmentation lose unique
features essential for the classification task and degrade models’
performance, as seen in Table III.

The proposed CapsNet-based model with InceptionV3 as convolution base is a
unique, performant and novel approach to glaucoma classification. While
attempts have been made with vanilla CapsNet or pre-trained CNN’s, but their
combination has not been previously attempted.

##  V Conclusion and Future Work

This work reproduces four state-of-the-art deep learning models for glaucoma
classification from fundus imagery. The utility of TL over data augmentation
under a limited training dataset scenario is demonstrated. The efficacy of
CapsNet for glaucoma classification is evaluated and the impact of
classification performance with changes in the convolutional layers and
routing iteration is investigated.

InceptionCaps, a novel DL model consisting of a InceptionV3 and capsule
network for glaucoma classification, is presented robustly and transparently,
detailing all its artifacts and model details. The model achieved an accuracy
of 0.956 and an AUC of 0.9556, outperforming several SOTA CNNs. The result is
promising, and the model has great potential to assist ophthalmologists. The
code for implementing this work is available from
https://github.com/gyanendar/InceptionCaps.

The completion of this work was fraught with difficulties. Several research
papers that describe automatic glaucoma classification from fundus images use
private datasets and omit key information about the model architecture. As a
result, those studies’ findings cannot be independently validated. The results
of four separate SOTA models were replicated using publicly available datasets
to ensure their applicability.

Furthermore, no standard benchmark indicators are employed to assess the model
performance uniformly, making the comparison of the models impossible.
Consequently, multiple performance metrics were utilised in order to compare
and contrast model performance.

Future work will include training the InceptionV3 model on a large retinal
image dataset for InceptionCaps and should be tested on a diverse dataset to
demonstrate its usefulness for glaucoma classification. The CNNs are complex
models and increase the computational resource necessary. More work is needed
to optimize the models’ computational resource requirement by compressing the
model through pruning and quantization.

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