# Cataract Fundus Image Detection Using Hybrid Deep Learning Model

J. Manikandan, Brahmadesam Viswanathan Krishna, C. Sasivarma, R.

Yogesh Kumar, M. Sidharth Raj

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J. Manikandan, Brahmadesam Viswanathan Krishna, C. Sasivarma, R. Yogesh Kumar, M. Sidharth Raj. Cataract Fundus Image Detection Using Hybrid Deep Learning Model. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.300-313, 10.1007/978-3-031-38296-3_23. hal-05466031

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# Cataract Fundus Image Detection Using Hybrid Deep Learning Model

J. Manikandan(✉), Brahmadesam Viswanathan Krishna, C. Sasivarma, R. Yogesh Kumar, and M. Sidharth Raj

Rajalakshmi Engineering College, Thandalam, Chennai, India
jmanekandan@gmail.com, {sasivarma.c.2019.cse, yogeshkumar.r.2019.cse, sidharthraj.m.2019.cse}@rajalakshmi.edu.in

**Abstract.** The overall prevalence of blindness in India is 1.1%, with cataract (62.6%) accounting for approximately 9 million cases. Cataracts are responsible for roughly 48% incidents of blindness worldwide. In order to develop an effective toolkit for detecting cataract, current research focusses for the development of a Hybrid Deep Learning Algorithm termed GDCNN which act as an efficient detection tool of cataract. For accurate detection current research work used cataract dataset which was taken from Kaggle repository. The proposed GDCNN comprised of two important building blocks. In the very first step, Generative Adversarial Network (GAN) is utilised to generate synthetic images, which primarily targets to produce more image data that are to be further used for the classification process. For effective classification Deep Convolutional Neural Network (DCNN) is adapted, which accurately learn features from the input image and produces the most reliable outcome. As the result our proposed GDCNN produces the highest accuracy of 99.21% which is comparably better outcomes than state-of-art approaches.

**Keywords:** Cataracts · Deep learning · Generative Adversarial Network (GAN) · Deep Convolutional Neural Network (DCNN)

## 1 Introduction

About nearly 1 million people across the globe have some type of visual disorder, per the World Health Organization [1]. Of those, 39 million have severely impaired eyesight and the rest have poor eyesight at best. Three-hundred-and-thirteen percent of all cases of vision problems and fifty-one percent of all cases of blindness can be traced back to cataract. Flaxman et al. [5] estimated that by 2020, that there would be 235.1 million with mild to severe vision problems (MSVI) and 39.5 million persons who were completely blind. A cataract would impact 57.1 million (24%) and 13.4 million (35%) amongst them. Global blindness will increase to surpass 40 million by 2025[6]. Vision distortion is one of the most frequent eye conditions caused by cataracts.
The greatest method for reducing risk and preventing blindness is accurate and prompt identification of cataracts. Regarding this fundamental problem, recent research has focused on cataract detection systems based on artificial Intelligence. A system that can operate efficiently without a lot of equipment, highly trained medical personnel, or a lot of time is desperately needed.

To put it simply, image processing is the process of applying digital computing techniques to a two-dimensional image. The bits that make up a digital image represent a sequence of real numbers. Removing noise and other imperfections from a digital image is what is meant by “analysing the digital image”. Transformation function, the frequency or grey level of an image is indeed the amplitude of the feature f(x,y), in which x and y are spatial (plane) position vector. In the last few decades, many different image processing approaches have been devised. Availability of high-powered personal computers, large-capacity memory devices, software applications, etc., has contributed to the rise in popularity of computer vision applications. There are many applications for image processing; in our investigation, we use electronic healthcare images, so we process them digitally. Pre-processing and enhancement are two types of image processing. Three methods of image analysis: segmentation, feature extraction, and classification [2].

Both computer vision and Learning Techniques have undergone tremendous developments recently. Medical applications of ML and DL include medical image analysis, perception, computer-aided prognosis, segmentation techniques, etc. Efficient feature representation and feature extraction is achieved by employing a variety of ML and DL models. Deep learning and machine learning techniques are useful for early diagnosis and prevention of disease. These methods help doctors better evaluate the effects of genetic variants on disease. The field of deep learning has recently attracted a lot of interest. Industries as diverse as retail, financial services, transport, production, medical services, and more are experiencing profound changes because of these innovations. One sector that makes extensive use of such tools is the healthcare sector. Due to the importance placed on maintaining good health, medical professionals are always looking for innovative methods to apply cutting-edge research to improve patient outcomes. To continue serving the healthcare sector, deep learning is helping medical professionals uncover previously hidden possibilities in data. Professionals in the medical field are always looking for novel ways to apply cutting-edge technologies for maximum effect. The use of deep learning in medicine allows for more precise diagnosis and improved treatment of all diseases [3].

To create the fake medical image, the GAN technique is used to learn from the initial database. The effectiveness of the CNN model for cataract identification is enhanced by using images generated with the planned GAN algorithm. The steps involved in developing the GAN algorithm we plan to implement in our proposed work are outlined below. Pixel transition is an example of a difficult change that goes beyond the usual data augmentation techniques of scaling, rotation, morphing, and flipping. Typically, this is accomplished by employing the methods Generative Adversarial Network (GAN) and Wasserstein GAN (WGAN). The main contribution made by this research is that it employs the CNNGAN algorithm to generate synthetic retinal images. Additionally, a
CNN algorithm developed for distinguishing between healthy and cataract-affected eyes
[4].

The GAN technique is used to produce artificial clinical data using the foremost database as input. The CNN algorithm improved in its ability to detect cataracts when compared to images obtained using a GAN algorithm.

## 1.1 Contribution of The Current Research

To handle data imbalanced issue and efficient classification task, the proposed work aims to build hybrid deep learning model which utilizing Generative Adversarial Network (GAN) and Deep Convolutional Neural Network (DCNN).

* Initially, Generative Adversarial Network (GAN) is utilized to address the data imbalanced problem, which has a typically works to generate synthetic images to overcome the data scarcity issue with the medical images.

* Then for effective classification of cataract, Deep Convolutional Neural Network (DCNN) is used.

# 2 Literature Review and Related Works

Utilizing a computer-aided automatically generated cataract detection method, ordinary, slight, mild, and serious cataract stages can be identified from a fundus image, as proposed by Yanyan Dong et al. [5]. With the proposed method, a neural network convolutional (CNN) trained on other data can be used to automatically categorize cataracts. After features have been extracted with a pre-trained convolutional neural network (CNN) model, they are fed into a support vector machine (SVM) classification model. After four steps, the classification accuracy is at 92.91%. In addition, Hongyan Zhang et al. [6] suggested an automated cataract grading technique that employs a high-level feature extracted from a residual network (ResNet18). Texture is revealed as a hidden attribute by means of the grey level co-occurrence matrix (GLCM). The cataract is again automatically graded using the information extracted from the image. The final performance of the classifier is produced by a fully connected neural network (FCNN), which consists of convolutional and pooling layers and acts as the meta-learner for the image. Two SVM classifiers are employed as base-learners to calculate the probability output data of every fundus image. Results from applying the proposed method have shown an accuracy rate of 92.66 percent across all six grading levels, with the accuracy rate achieving 93.33%.

To analyze how well Deep Convolutional Neural Networks (DCNNs) perform in fully autonomous sensing and classifying cataract [7]. With the DCNN feature extraction used, the highest accuracy was achieved in both cataract detection and rating (93.52% and 86.69%, respectively). This was made possible by visualising the high-order evidence based semantic information of a few of the extracted features at the pool-5 layer. Using ocular images captured by a cellphone slit lamp, shenming hu et al. [8] explored and evaluated the classification efficiency of the computer controlled nuclear cataract detection technique. The ocular lens nucleus was pinpointed using YOLOv3. In contrast, CataractNet is used for automated cataract detection in fundus images. A total of
1130 fundus images, both of cataracts and of healthy eyes, were collected and expanded to 4746 images for model training [9]. The problem of over-fitting can be avoided by augmenting the dataset before model training begins.

Using a hidden tubular structured interpretation that includes the bitwise vessel architecture, this research [9] attempts to generate some many retinal (or neural) images with feasible characters. The created phantoms are expected to look and feel similar to the training image, which means they will keep their tubular structure. To aid clinical judgement in an IoT-based healthcare system, researchers have developed a novel semi-supervised learning approach using generative adversarial networks (GAN) [10]. Specifically, they use GAN to generate more labelled data and compensate for totally unbalanced labelled classes with additional falsified data, both of which improve the effectiveness of semi-supervised learning in their approach. Based on our findings, we recommend GL-Net [11], a multi-label DCNN design that makes use of generative adversarial networks. GL-Net is comprised of two distinct network architectures, the Generator and the Discriminator. We use skip correlation in the Generator to promote the integration of course and fine-grained feature data.

To facilitate more open information sharing and the development of Advanced analytics, the author of Study [12] proposes a novel method for generating simulated data that strongly matches the joint variation distribution inside a previous EHR dataset. An image encryption structure is proposed in the form of a distortion function [13]. It consists of three parts: a generator that employs a U-Net structure to transform an image into an embedded change likelihood map; the map's embedding likelihood; and the image itself. As stated in [14], the author created a novel pyramid learning algorithm. In this experiment, we use the RMSProp method to maximize the effectiveness of the network training phase. To learn the most obvious distinction between both the denoised and normal-dose images, the GAN-based noise removal methodologies [15] typically employ a second classification network, or discriminator.

## 3 Proposed Methodology

Current research proposes the hybrid approach which encompasses of GAN and DCNN. To produce artificial clinical data from the source data set and get around the problem of medical data scarcity, we are firstly using the GAN framework in the pre-processing stage. The Convolutional Neural Network (CNN) is employed for the extracting features task in the second phase. In this stage, a convolution moves the window via images before computing its input and filter inner product pixel values. CNN also uses max consolidating to substitute output with a max summary in to decrease size of the data and batch processing. This lowers the possibility of overfitting and enables you to identify the features that have the greatest influence. Convolution can then accentuate the important traits as a result. It after every convolutional and maximum pooling layer in the classification process of CNN, we can use the Rectified Linear Unit (ReLU). For values of x > 0, the ReLU function simulates the activations of our neurons on a "large enough stimulus" to cause nonlinearity and returns 0 otherwise. Dimishing gradients have been successfully solved using this method. Very light weights will still be 0 just after ReLU activation function (Figs. 1 and 2).
**Algorithm 1: Algorithm for Proposed workflow.**

**Input:** Cataract affected fundus images

**Output:** Cataract identification

**Step 0:** START

**Step 1:** Fetch the retinal image Data set.

**Step 2:** Check for the Data scarcity issue with the fetched medical Data set. If yes, then proceed to Step 3 else proceed to Step 4.

**Step 3:** Apply GAN algorithm to the Image Data set and generate more synthetic images to overcome the Data scarcity issue.

**Step 4:** Now, for the classification purpose Apply CNN algorithm over the medical data set.

**Step 5:** Train the CNN model with the help of the available image data set and proceed to step 6.

**Step 6:** Classify the images into Cataract affected eye and normal Eye.

**Step 7:** Showcase the end results.

**Step8:** STOP

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Proposed work.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 2. Architecture of the proposed work.</figcaption></figure>

## 3.1 Generative Adversarial Network

Deep neural network topologies made up of two networks are known as “generative adversarial networks,” or GANs. One is referred to as the generator, while the other (the foe) is referred to as the discriminator. The discriminator is trained to distinguish created or synthetic data from actual data, and the two networks engage in a game where the generator is trained to create realistic examples. They are trained concurrently, and due to the competition, the artificial samples become identical to the real data. According to the theory of GAN, the performance of Discriminator and Generator continuously optimizes parameters by iterative training [15].

$$ \min_{G} \max_{D} V(D, G) = E_{X \sim f_{\text{data}}(x)}[\log D(x)] + E_{h \sim f_{h}(h)}[\log(1 - D(G(h)))] \quad (1) $$

Task of generator is to convert an input noise variable (z) into the desired data space (x) when the generator G is intended to create samples (G(z)) from the generator distribution (PG). In order to train this generator, samples from the genuine data distribution Fdata and the generator's distribution PG are played against an adversarial discriminator D. Therefore, the best discriminator for a given generator is $D(X) = Fdata(x)/(Fdata(x) + FG(x))$, and the best generator is attained when $FG(x) = Fdata(x)$.

### 3.1.1 Generator

The Generator's role is to guess the probabilistic model of the actual data datasets, so that it can produce new samples that are statistically very close to the actual data. There are a total of 19 layers in the Generator's fully convolutional architecture. The input to this system is an image of the retina, denoted by x. Generator (left side) and demodulator (right side) make up the network structure (right side). The encoder uses
a VGG16-style network to pull features from the retinal image. The original VGG16 network uses a sub-sampling attribute of 32, but this can lead to feature loss if it's used too often. When down simple random sample is set to 32, all input about areas of the optic disc with a resolution of 32 pixels or less is lost. Adding so much up samplings also adds more work in terms of calculations and variables. As a result, we reduced the number of parameter estimation and computations by eliminating the very last two down sample selection layers and increasing the sub-sampling factor to 16.

$$ \text{Generatorloss: } L^{(G)} = \min[\log(D(x)) + \log(1 - D(G(z)))] \quad (2) $$

### 3.1.2 Discriminator

The discriminator made a forecast regarding the likelihood of the samples emerging from the legitimate data as opposed to the data that was produced by the generator:

A network model with 8 layers serves as the discriminator's underlying network structure. As training dataset, retinal image 'x' and ground truth 'y', abbreviated as G(x), are aggregated together. Also, every layer makes use of a depth wise convolution with a stride value of two rather than a pool layer. After each iteration of the convolution, the dimensions of the feature space are shrunk to one-quarter of its initial dimensions. We normalise the insight of a previous layers using batch normalisation in the convolution operation so that we can quicken up the convergent process and minimize the influence of the initialising weight on the network structure. This allows us to alleviate the effect that the initialising weight has on the network structure:

In the end, the completely connected layer is the one that serves as the connection. The discriminator would then output the multivariate statistical result, which would be either 0 or 1. Based on the difference in results between both the discriminator as well as the label, the purpose of the discriminator is to recognise the origin of the retinal image and ocular cup label in the retinal image to the greatest extent possible:

$$ \text{Discriminator loss function: } L^{(D)} = \max[\log(D(x)) + \log(1 - D(G(z)))] \quad (3) $$

## 3.2 Deep Convolutional Neural Network

Processing structured storage systems of data such as images requires a special kind of deep learning system that is referred to as a fully convolutional network is utilised. The most advanced method currently available for a wide variety of visual application domains, such as classification tasks. In the field of computer vision, the use of convolutional networks is widespread. Convolutional neural systems are very good at recognising the patterns that are present in the input image [16]. These patterns include eye lids and continues to face, in addition to lines, contours, and groups. Due to this property, fully convolutional networks are particularly useful for applications involving computer vision. In contrast to more traditional approaches to computer vision, the use of convolutional networks does not necessitate any sort of image preparation and can begin processing a raw image right away. The features of convolutional layers include the convolutional, the max pooling, the normalization layers, and the flattening layers.
### 3.2.1 Convolutional Layer

During the methodology of convolutions, a n*m kernel (where n is greater than zero and m is greater than zero) analyses the input image in order to carry out an automated feature extraction procedure. When a filter that's also narrower than the image is applied to the image and then superimposed on it, the result is a feature map. After that, a stride value determines how the filter should be moved throughout the image. As you make further strides, the magnitude of a convolution layer will continue to shrink. During the convolution process for strides greater than or equal to 2, some of the picture's finer details are lost. Trying to combine a wide variety of kernels allows us to generate many feature maps while preserving the greatest range of features possible. The ReLU step comes immediately after the fully-connected step. Its purpose is to reduce computational burden while simultaneously increasing the model's nonlinearity.

$$z_{i,j,k} = b_k + \sum_{u=0}^{f_h-1} x \sum_{v=0}^{f_w-1} y \sum_{k'=0}^{f_{n'}-1} x_{i',j',k'}.w_{u,v,k',k} \quad (4)$$

### 3.2.2 Pooling Layer

Pooling, also known as down sampling, is performed on the convolution layer in order to reduce the amount of memory required by the model while also ensuring that the CNN is able to detect its same instance in images of varying forms. There are several distinct varieties of pooling, the most common of which are the highest, minimal level, average (sometimes referred to as sub-sampling), and sum. The areas of the image with the highest amount in the feature space are those from which the image's features were compared to each other with the greatest similarity; consequently, max pooling maintains the qualities that are most valuable.

### 3.2.3 Flatten Layer

To provide input to a NN for additional calculations, the pooled feature map is flattened into a column matrix.

### 3.2.4 Fully Connected Network Layer

Following the process of flatness, the final stage of a CNN is an Artificial Neural Network (ANN) that is made up of input nodes, hidden units, and output neurons. In contrast to ANNs, CNNs require even the hidden layers to be fully connected in order to satisfy the requirement that all layers have to be fully linked. Nevertheless, this is not a hard and fast requirement in ANNs. Both the weights and the feature detectors are subject to alterations to facilitate improvements in performance during backpropagation:

### 3.2.5 Loss Function

Cost functions are analogous to those found in ANNs, and CNNs have something called the Loss function. When it comes to classification jobs, the cross-entropy feature is by
far the most common Loss function to use. However, this is only helpful for classification purposes. The Categorical Cross-Entropy Loss method is the most effective choice for regression. In situations in which there are more than two categories, we make use of a statistic called unambiguous cross-entropy.

$$CELoss = -\frac{1}{n} \sum_{i=1}^{N} y_i \sum_{j=1}^{M} y_{ij} \cdot \log(p_{ij}) \quad (5)$$

where p is a function of the target classes and q is a function of the CNN output. The Normalized exponential function, commonly known as the SoftMax function, is typically inserted at the output layer of CNNs. The output of the logits must be constrained to the range [0,1].

$$f_j(x) = \frac{e^{x_j}}{\sum_k e^{x_k}} \quad (6)$$

# 4 Experiment and Result Discussion

This section demonstrates the various activities which are done regarding our implementation. The research focuses to build a Hybrid deep learning model termed GDCNN, which is the fusion of GAN and Deep Convolutional Neural Network (DCNN). Here GAN is used to generate synthetic images to overcome the data scarcity issues. Following that DEEP-CNN utilized for classification task.

Five Pre-Trained CNN models are included in our finding's comparison. First, we contrasted the performance of the MobileNet algorithm with that of the Inception-v-3 method. After then, we compared MobileNet's performance to that of VCG-16 and looked at it. The performance of VCG-16 and VCG-19 was then compared, and its performance was also examined. The performance of VCG-19 was then compared to that of ResNet-50, and even its performance was examined. The final comparison of ResNet-50 and Proposed Model uses the original fundus picture dataset to track classifier development.

The implementation of proposed work is done using Windows 10 operating systems, Intel® Core™ i5 processor having 8 GB RAM capacity and NVIDIA GeForce GTX 1650Ti. Also, Python language is used for implementation purposes and Google Co-lab is used for training the model and classifying the diseases (Fig. 3).

## 4.1 Dataset Description

This study analyzed with a dataset, all of which were derived from publicly accessible databases. The dataset included in this research comprised of 400 fundus images obtained is available in the following repository, which clearly is the open source: https://www.kaggle.com/datasets/jr2ngb/cataractdataset. Among the dataset, 300 which consists of Normal Eye and 100 which consists of Cataract Eye. And the entire images holds both Cataract patients and healthy individuals. Totally two classes which are existing in this dataset which includes Cataract and Normal. Each class consisting of different number of images. For instance, class Normal comprises maximum of 300 images and class Normal comprises of only 100 images. The datastore is depicted in Fig. 4 (Table 1).
<table><caption>**Table 1.** Properties of dataset</caption><thead><tr><td>Properties of dataset</td><td>Respective values</td></tr></thead><tbody><tr><td>Image size</td><td>2592 × 1728pixel</td></tr><tr><td>Nature of image</td><td>RGB</td></tr><tr><td>Entire classes</td><td>3</td></tr><tr><td>Cataract Eye</td><td>100</td></tr><tr><td>Normal Eye</td><td>300</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Original dataset</figcaption></figure>

Once GAN is applied over the dataset, the newly synthesized images are holding the properties which are given Table 2. Also, the respective generated images as given in Fig. 5.

## 4.2 Performance Analysis

Image classification research to use a variety of performance evaluation criteria that are widely used in other similar studies. There are a few of them, including accuracy, precision, specificity, recall, and sensitivity. The formulas and related numbers for each of these metrics are provided below (Table 3).

$$ accuracy = \frac{\sum TP, TN}{\sum TP, TN, FP, FN} \qquad (7) $$
<table><caption>**Table 2.** Properties of dataset (After GAN applied)</caption><thead><tr><td>Properties of dataset</td><td>Respective values</td></tr></thead><tbody><tr><td>Image size</td><td>512 × 512pixel</td></tr><tr><td>Nature of image</td><td>RGB</td></tr><tr><td>Entire classes</td><td>3</td></tr><tr><td>Cataract Eye</td><td>1038</td></tr><tr><td>Normal Eye</td><td>1074</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Images generated after GAN is applied.</figcaption></figure>

$$ \text{Specificity} = \frac{TN}{\sum TN, FP} \qquad (8) $$

$$ \text{Sensitivity} = \frac{TP}{\sum FP, FN} \qquad (9) $$

$$ \text{Precision} = \frac{TP}{\sum TP, FP} \qquad (10) $$

$$ \text{Recall} = \frac{TP}{\sum TP, FN} \qquad (11) $$
<table><caption>**Table 3.** Performance of the proposed technique with respect to the state of art of approaches</caption><thead><tr><th>Methods</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td>Inception v3 [17]</td><td>86.13</td><td>88.79</td><td>83.49</td><td>86.28</td><td>87.99</td></tr><tr><td>MobileNet [18]</td><td>87.23</td><td>88.16</td><td>84.51</td><td>85.76</td><td>88.87</td></tr><tr><td>VGG-16 [19]</td><td>94.67</td><td>86.19</td><td>92.86</td><td>96.49</td><td>94.34</td></tr><tr><td>VGG-19 [20]</td><td>95.99</td><td>86.68</td><td>93.76</td><td>97.83</td><td>96.13</td></tr><tr><td>ResNet-50 [21]</td><td>96.34</td><td>95.62</td><td>97.43</td><td>96.89</td><td>96.78</td></tr><tr><td>DCNN (without GAN)</td><td>94.32</td><td>94.65</td><td>97.01</td><td>96.92</td><td>97.43</td></tr><tr><td><strong>GDCNN</strong></td><td><strong>99.21</strong></td><td><strong>98.21</strong></td><td><strong>98.98</strong></td><td><strong>98.56</strong></td><td><strong>98.19</strong></td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Comparison graph</figcaption></figure>

## 5 Conclusion

The current research proposes an automated cataract detection system which we have named as GDCNN. To detect cataract, the earlier researchers focused mostly on CNN and its variants. Though it produces good outcomes based on the dataset, most of the time it suffers due to data scarcity issue. Hence in our current research, we have suggested Generative Adversarial Network (GAN) to overcome the data scarcity issues. Here GAN generates several synthetic images which can be used to train the DCNN to perform well. Deep Convolutional Neural network is used for accurate classification of cataract. As an outcome we have achieved Accuracy of 99.21%, Precision of 98.21%, Recall of 98.98, Specificity of 98.56, Sensitivity of 98.19. Also, comparisons have been made, which shows our proposed approach peaks all the other existing state-of-the-art approaches.
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