# Diabetic foot ulcers monitoring by employing super resolution and noise
reduction deep learning techniques

Agapi Davradou  National Technical University of AthensGreece
<EMAIL_ADDRESS>,  Eftychios Protopapadakis  National Technical
University of AthensGreece<EMAIL_ADDRESS>,  Maria Kaselimi  National
Technical University of AthensGreece<EMAIL_ADDRESS>,  Anastasios
Doulamis  National Technical University of AthensGreece<EMAIL_ADDRESS>and
Nikolaos Doulamis  National Technical University of AthensGreece
<EMAIL_ADDRESS>
(2022)

###### Abstract.

Diabetic foot ulcers (DFUs) constitute a serious complication for people with
diabetes. The care of DFU patients can be substantially improved through self-
management, in order to achieve early-diagnosis, ulcer prevention, and
complications management in existing ulcers. In this paper, we investigate two
categories of image-to-image translation techniques (ItITT), which will
support decision making and monitoring of diabetic foot ulcers: noise
reduction and super-resolution. In the former case, we investigated the
capabilities on noise removal, for convolutional neural network stacked-
autoencoders (CNN-SAE). CNN-SAE was tested on RGB images, induced with
Gaussian noise. The latter scenario involves the deployment of four deep
learning super-resolution models. The performance of all models, for both
scenarios, was evaluated in terms of execution time and perceived quality.
Results indicate that applied techniques consist a viable and easy to
implement alternative that should be used by any system designed for DFU
monitoring.

diabetic foot ulcer, neural networks, noise removal, super-resolution

††journalyear: 2022††copyright: acmlicensed††conference: The15th International
Conference on PErvasive Technologies Related to Assistive Environments; June
29-July 1, 2022; Corfu, Greece††booktitle: The15th International Conference on
PErvasive Technologies Related to Assistive Environments (PETRA ’22), June
29-July 1, 2022, Corfu, Greece††price: 15.00††doi:
10.1145/3529190.3529214††isbn: 978-1-4503-9631-8/22/06††ccs: Computing
methodologies Reconstruction

##  1\. Introduction

The ”prevention is better than cure” principle applies aptly in diabetic foot
ulcer (DFU) and can be achieved by motivating patients towards self-
monitoring. People involved in self-management can help prevent/postpone the
appearance of an ulcer, by detecting the corresponding signs and symptoms
early on. Additionally, monitoring of existing ulcers is advised to prevent
complications or recurrent ulceration (Armstrong et al., 2017). During DFU
monitoring, there are various signs and symptoms that should be taken under
consideration including: skin color change (redness), skin temperature change,
foot pressure induced injury (damage to the skin and/or underlying soft
tissue), pain, swelling, or odor.

Today, most of these DFU indication signs can be captured and, consequently,
monitored, using various optical sensors (Doulamis et al., 2021), such as
those integrated in mobile devices (Goyal et al., 2018b) . It is intriguing
the fact that RGB and thermal sensors can support DFU monitoring. Some major
advantages of these type of sensors involve the, relatively, low acquisition
costs, compact structure, and easy integration to portable devices. Typically,
the raw data provided by the sensors are fed to complex AI tools, which serve
as decision making mechanisms. There are multiple studies advocating that AI
tools, coupled with optical sensors, can provide extremely useful decision
support mechanisms. Such mechanisms can assist both physicians and patients to
prevent undesirable situations (Tulloch et al., 2020).

In this paper, we focus on the AI tools for image-to-image translation
techniques (ItITT) (Kaji and Kida, 2019). This scenario considers the steps
prior to any decision making. The main research question focuses on how ItITT
may support decision making and monitoring of DFU, given a set of images,
provided by devices. Two cases of ItITT are considered: a) noise reduction and
b) super-resolution (SR). Common reasons for low-quality images in wound
documentation may appeared due to poor focus, motion blur, occlusion,
inadequate lighting, and backlight, or due to time constraints associated with
treatment and documentation, even when the image capturing is performed by
trained personnel (Yap et al., 2021b). Improving the quality of a medical
image is crucial to support the decision making and diagnosis (Oulhaj et al.,
2012).

The remainder of the paper is structured as follows: section 2 presents a
brief background on noise removal using autoencoders and super-resolution
techniques in medical imagery data. Section 3 describes the proposed deep
learning tool to improve the quality in medical images for diabetic foot ulcer
monitoring. In Section 4, an extensive experimental evaluation of the
discussed methods is provided, while Section 5 closes the paper with a summary
of findings.

##  2\. Related work

Applying deep learning techniques for ulcer detection (Yap et al., 2021b) and
segmentation (Goyal et al., 2017), as well as classification of infection and
ischaemia (Goyal et al., 2020), (Yap et al., 2021a) of diabetic foot ulcers,
has shown great potential. Yet little emphasis was given for
preprocessing/image enhancement steps. Several attempts have been made in
order to tackle the image denoising problem using deep learning (DL) and
autoencoders have been one of the main applied techniques (Voulodimos et al.,
2018). In (Vincent et al., 2008) denoising autoencoders were presented, in
order to learn features from noisy images, while (Burger et al., 2012) used
stacked denoising autoencoders to reconstruct clean images from noisy images
by exploiting the encoding layer of the multilayer perceptron (MLP). In (Xie
et al., 2012) Gaussian noise removal was achieved, by applying stacked sparse
denoising auto-encoders and (Gondara, 2016) showed that denoising autoencoders
constructed using convolutional layers can be efficiently used to denoise
medical images. In (Liu and Zhang, 2018) stacked sparse denoising autoencoders
were used to reduce the noise and improve the overall imaging quality of low-
dose computed tomography (CT) images and (Ghosh et al., 2019) applied stack
denoising convolutional autoencoder for retinal image denoising.

Conventional SR methods learn the dictionaries (Timofte et al., 2013) or
manifolds (Bevilacqua et al., 2012) for modeling of the patch space. On the
other hand, deep neural network (DNN)-based methods learn an end-to-end
mapping between low- and high-resolution images, thus implicitly achieving
dictionaries or mapping functions for patch space by hidden layers of DNN.
With SRCNN (Super-Resolution Convolutional Neural Network), low-resolution
input images are first upscaled to the desired size by use of bicubic
interpolation and are then fed to an encoder-decoder network; thus, end-to-end
mapping between the bicubic upscaled version of a low-resolution image and a
ground truth high-resolution image is learned.

The SRCNN has been applied to mammography images (Umehara et al., 2017) and
chest CT images (Umehara et al., 2018). SRGAN (Super-resolution using a
generative adversarial network) (Ledig et al., 2017) can be thought of as a
generative adversarial network (GAN)-fortified version of SRCNN. An encoder-
decoder network with more upsamling layers than downsampling layers is trained
to recover detailed textures from heavily downsampled images, and a
discriminator is trained to differentiate between the super-resolved images
and original high-quality images. SRGAN has been applied to the generation of
high-resolution brain magnetic resonance imaging (MRI) images from low-
resolution images, and 3D convolution has been adopted for exploiting
volumetric information (Sánchez and Vilaplana, 2018).

##  3\. Models’ architecture

###  3.1. Noise Removal Tool

In this scenario, the capabilities on noise removal were investigated, for a
convolutional neural network stacked-autoencoders (CNN-SAE) over RGB images,
induced with Gaussian noise. The concept imposes the generation of random
noise over multiple images, the training of a CNN-SAE model and the
application over multiple test sets (i.e., previously unseen images). The use
of high variance values have been intentionally selected, in order for the
noise to stress test the model capabilities.

Figure 1. Training phase illustration for the deep learning model.

Training phase illustration for the deep learning model.

Figure 1 demonstrates the steps during the training process. Initially, an RGB
image, denoted as $I$, is split in smaller patches $I^{(k)}$, of size 256×256
pixels each. Then, Gaussian noise of $(\mu,\sigma)=(0,0.3)$ is applied to each
of the patches, creating a new (noisy) image patch $\tilde{I}^{(k)}$. Then,
these pairs ($I^{(k)}$,$\tilde{I}^{(k)}$) are gathered and serve as a training
set for the deep learning (DL) model.

Figure 2. Testing phase illustration for the DL model.

Testing phase illustration for the DL model.

Figure 2 demonstrates the testing phase for the model. The adopted approach
allows for the fast implementation of the proposed CNN-SAE scheme over any
type of image. The stitching part, i.e., merging the denoised patches back to
a whole image can be further refined to mitigate the lining patterns.

###  3.2. Super-resolution Tool

Four deep learning techniques were selected and investigated to tackle the
super-resolution task: SRGAN (Ledig et al., 2017), EDSR (Lim et al., 2017),
ESRGAN (Wang et al., 2018) and ISR (et al., 2018) . The pipeline for the
utilization of the super-resolution methods is illustrated in Figure 3.

Figure 3. The implemented deep learning super-resolution evaluation scheme.

The implemented deep learning super-resolution scheme.

As mentioned in section 2, the SRGAN is a GAN for image super-resolution for
which a deep residual network (ResNet) (He et al., 2016) with skip-connection
was employed. The authors proposed a new perceptual loss function, which
consists of an adversarial loss and a content loss. The adversarial loss uses
a discriminator network, that is trained to differentiate between the super-
resolved images and original photo-realistic images, in order to push the
solution to the natural image manifold. The content loss was motivated by
perceptual similarity instead of similarity in pixel space and is calculated
on high-level feature maps of the VGG network (Johnson et al., 2016), which
are more invariant to changes in pixel space (Li and Wand, 2016).

The ESRGAN is an enhanced version of SRGAN in three aspects. First, the
network structure was improved by introducing the residual-in-residual dense
block (RDDB), without batch normalization (BN) (Ioffe and Szegedy, 2015)
layers as in (Lim et al., 2017). Second the discriminator was improved using
relativistic average GAN (RaGAN) (Jolicoeur-Martineau, 2018), which learns to
judge the relative realness of an image, instead of the absolute value,
whether it is real or not. Finally, the perceptual loss was also improved by
using the VGG features before activation instead of after activation.

The EDSR is an enhanced deep super-resolution network based on the SRResNet
(Ledig et al., 2017) architecture. The model is optimized by removing
unnecessary modules in conventional residual networks. In addition, scale-
independent information is utilized during the training phase, by training
high-scale models from pre-trained low-scale models.

Finally, the ISR model performs image super-resolution by combining
implementations of different residual dense networks. More specifically, the
model utilizes the SRGAN custom discriminator network, a multi-output version
of the VGG19 network for deep features extraction for the perceptual loss, the
super-scaling residual in residual dense network of ESRGAN and finally, the
super-scaling residual dense network described in (Zhang et al., 2018).

##  4\. Experimental setup

###  4.1. Dataset description

In this work the DFUC 2020 dataset, from the Diabetic Foot Ulcers Grand
Challenge 2020 (DFUC 2020), is utilized (Cassidy et al., 2021), containing
clinically annotated infection and ischaemia cases and aiming on supporting
the development of DFU pathology recognition methodologies (Goyal et al.,
2018a). DFUC 2020 was a medical imaging classification competition hosted by
the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020
(Yap et al., 2020). This dataset is publicly available for non-commercial
research purposes only and can be obtained by emailing a formal request to the
authors. The DFUC 2020 dataset consists of 4,000 images, with 2,000 used for
the training set and 2,000 used for the testing set. The training set consists
of DFU images only, and the testing set comprised of DFU images, other
foot/skin conditions and healthy foot images.

The images were captured during regular patient appointments at Lancashire
Teaching Hospitals foot clinics; therefore, some images were taken from the
same subjects at different intervals. Thus, the same ulcer may be present in
the dataset more than once, but at different stages of development, at
different angles and lighting conditions.

###  4.2. Performance metrics

Root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and
structural similarity index measure (SSIM) are used to evaluate both the
proposed encryption and decryption algorithms and the super-resolution models
quality (Sara et al., 2019).

Generally, mean square error-based metrics are in common use as objective
measures of distortion, due mainly to their ease of calculation. MSE value
denotes the average difference of the pixels all over the image. A higher
value of MSE designates a greater difference between the original image and
processed image. Nonetheless, it is indispensable to be extremely careful with
the edges. The following equation provides a formula for calculation of the
MSE:

(1) $MSE=\frac{1}{N}\Sigma\Sigma\left(\tilde{I}_{ij}-I_{ij}\right)^{2},$

where $\tilde{I}_{ij}$ denotes the feature values for a specific pixel located
in i-th row, j-th column. $\tilde{I}$, $I$ correspond to reconstructed/resized
image and the original one, respectively. N denotes the number of pixels.

The root mean square error (RMSE) is given by as the squared root of MSE
(Asamoah et al., 2018):

(2) $RMSE=\sqrt{MSE}.$

The peak signal-to-noise ratio metric is commonly used to characterize
reconstructed image quality. The term PSNR is an expression for the ratio
between the maximum possible value (power) of a signal and the power of
distorting noise that affects the quality of its representation. Because many
signals have a very wide dynamic range (ratio between the largest and smallest
possible values of a changeable quantity), the PSNR is usually expressed in
terms of the logarithmic decibel scale. In this work, the assumption is made
that the input and output data are a standard 2D array of data or matrix. The
dimensions of the correct image matrix and the dimensions of the degraded
image matrix must be identical. The mathematical representation of the PSNR is
as follows:

(3) $PSNR=20\log_{10}\left(\frac{max(I)}{\sqrt{MSE}}\right).$

The structural similarity index (SSIM) is a perceptual metric that quantifies
image quality degradation (Dosselmann and Yang, 2011) caused by processing
such as data compression or by losses in data transmission. It is a full
reference metric that requires two images from the same image capture, a
reference image $I$ and a processed image $\tilde{I}$. The SSIM index is
defined as:

(4)
$SSIM\left(I,\tilde{I}\right)=\left[l\left(I,\tilde{I}\right)\right]^{\alpha}\cdot\left[c\left(I,\tilde{I}\right)\right]^{\beta}\cdot\left[s\left(I,\tilde{I}\right)\right]^{\gamma},$

where $\alpha>0$, $\beta>0$ and $\gamma>0$ control the relative significance
of each of the three terms of the index. The luminance $l(\cdot)$, contrast
$c(\cdot)$, and structural components $s(\cdot)$ of the index are defined
individually as:

(5)
$l\left(I,\tilde{I}\right)=\frac{2\mu_{I}\mu_{\tilde{I}}+C_{1}}{\mu_{I}^{2}\mu_{\tilde{I}}^{2}+C_{1}},$

(6)
$c\left(I,\tilde{I}\right)=\frac{2\sigma_{I}\sigma_{\tilde{I}}+C_{2}}{\sigma_{I}^{2}\sigma_{\tilde{I}}^{2}+C_{2}},$

(7)
$s\left(I,\tilde{I}\right)=\frac{2\sigma_{I\tilde{I}}+C_{3}}{\sigma_{I}\cdot\sigma_{\tilde{I}}+C_{3}},$

where $\mu_{I}$ and $\mu_{\tilde{I}}$ represent the means of the original and
coded images, respectively, $\sigma_{I}$ and $\sigma_{\tilde{I}}$ represent
the standard deviations, respectively, $\sigma_{I}^{2}$ and
$\sigma_{\tilde{I}}^{2}$ denote the variances, respectively, and
$\sigma_{I\tilde{I}}$ is the covariance of the two images. As a means of
dealing with the situations in which the denominators are close to zero, the
constants C1, C2 and C3 are introduced.

###  4.3. Experimental results

In the following two section quantitative results of the implemented noise
removal method, as well as the super-resolution algorithms, are presented and
compared.

####  4.3.1. Noise Removal Tool

An autoencoder model was developed, trained and evaluated on 4 separate test
sets.

Figure 4 illustrates the RMSE and PSNR scores of the autoencoder for each test
set. The model performs a RMSE of approximate 0.12 for all test sets and also
a stable minimum RMSE value of around 0.07. On the other hand, the maximum
RMSE value shows larger variations ranging from 0.18 to 0.27. Finally, the
autoencoder performs better on set 1, followed by its evaluation on set 3.
However, it should also be noted, that model’s performance on set 1 presents
the largest difference between its minimum and maximum values and set 3 the
smallest. The PSNR value ranges at approximately 17 for all test sets, with
performance on set 3 showing the worst score and set 1 the best. The minimum
PSNR values lie between 11-14, with the evaluation on set 1 having the lowest
of all and on set 3 the highest. The maximum PSNR value is stable around 23
and the results on set 4 show the highest and on set 2 the lowest.

Figure 4. RMSE and PSNR scores per test set.

RMSE and PSNR scores per test set.

Figure 5 illustrates the scores of the autoencoder evaluated on the SSIM
metric, as well as the minimum, maximum and average time the model needs to
denoise a single image for each test set. The model shows a great stability
with a SSIM value ranging between 0.68 and 0.7. The model performed worst on
set 4 and best on sets 2 and 3 which have the same value. The minimum SSIM
values lie around 0.56 and 0.6 and the maximum is almost stabilized at 0.78.
In overall, the model performed best on set 3, which has the smallest
difference between its minimum and maximum values, followed by the performance
on set 2. The minimum and maximum values range between approximately 3 – 4.5s
and the average times of the model lie between 3.5 and 3.8s. The model shows a
great stability in processing time in both the average and the extreme values,
as there are no large deviations when comparing the denoising times of all
sets.

Figure 5. SSIM scores and average processing time per test set.

SSIM scores and average processing time per test set.

Figure 6 provides some samples for the proposed approaches, including
different camera angles. The first row shows the original images of the
diabetic foot in different camera angles, the second row shows the corrupted
by the noise images and the final row illustrates the denoised images after
the implementation of the noise removal tools.

Figure 6. Demonstration of the noise remove capabilities. In (a) the original
images are shown, in (b) the ones with induced noise and in (c) the resulted
denoised images.

Demonstration of the noise remove capabilities. In column (a) the original
images are shown, in (b) the ones with induced noise and in (c) the resulted
denoised images.

####  4.3.2. Super-resolution Tool

In this section the four deep learning models implemented for super-resolution
are compared and quantitative results are presented. Figure 7 illustrates the
RMSE and PSNR scores for the four adopted models. The ISR technique achieves
the best RMSE score, with its value being value being just above 2 and its
minimum and maximum values being 1 and 3, respectively. The SRGAN model on the
other hand, achieves the worst RMSE score, which exceeds 7 and a maximum value
above 16. The second-best technique is the EDSR having a value of almost 4,
followed by the ESRGAN model with a value around 5. The best PSNR score is
achieved by the ISR method with its maximum value exceeding 45 and its minimum
being almost 35. The EDSR model shows the second highest PSNR with its maximum
and minimum values being around 42 and 30, respectively, followed by the
ESRGAN with a PSNR of 35. Finally, the SRGAN showed the worst PSNR
performance, with a PSNR of 30 and its maximum value being just above 35.

Figure 7. RMSE and PSNR scores for the adopted techniques.

RMSE and PSNR scores for the adopted techniques.

In figure 8 the super resolution methods are evaluated using the SSIM metric,
as well as, the time (seconds) required to increase the resolution of an
image. The results show that the ISR technique achieves the highest SSIM score
which exceeds 95% and with its minimum and maximum values lying between 93%
and 99%. The EDSR and ESRGAN methods achieved also high SSIM scores of 93% and
90%, respectively. The ESRGAN shows a noticeable divergence between its
minimum and maximum values. Finally, the SRGAN technique shows the worst
performance with a SSIM value below 80%. The time (seconds) required to
increase the resolution of an image ranges between 1.8 and 7 seconds.
Regardless of the selected approach, all the suggested schemes appear
appropriate for use in hospital or home environment. Yet, the processing time
is strongly depended on the available hardware.

Figure 8. SSIM scores and average enhancement time, per proposed model.

SSIM scores and average enhancement time, per proposed model.

Figure 9 illustrates the difference in quality among different images,
enhanced using suggested models. Inter_area corresponds to a traditional
linear approach and is used for demonstration purposes. ISR model performed
better compared to the competition but required more processing time.

a | b | c | d | e
---|---|---|---|---

Figure 9. Comparison among Inter_area approach (a) and the predicted images
for the ISR (b), EDSR (c), SRGAN (d) and ESRGAN (e) deep learning techniques.

Comparison among Inter_area approach (a) and the predicted images for the ISR
(b), EDSR (c), SRGAN (d) and ESRGAN (e) deep learning techniques.

##  5\. Conclusion

In this paper we focus on image-to-image translation techniques, which can
support decision making and monitoring of diabetic foot ulcers, given a set of
images, provided by RGB sensors. Two cases of ItITT are considered: a) noise
reduction and b) super-resolution. Starting with the noise removal tool, we
have adopted an CNN-SAE model. The architecture could reconstruct the initial
corrupted image despite the high standard deviation in induced noise. Then,
for the resolution improvement, the ISR super-resolution tool appears to be
the most prominent candidate. Processing times are less than few seconds, per
image, but may vary, depending on the hardware specification of the deployed
device.

###### Acknowledgements.

The work in this paper has been supported by the H2020 Phootonics project: “A
Cost-Effective Photonics-based Device for Early Prediction, Monitoring and
Management of Diabetic Foot Ulcers” funded under the ICT H2020 framework and
the grand agreement no. 871908.

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