IRJET - Eye Disease Detection using RESNET

International Research Journal of Engineering and Technology (IRJET)

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Cataract Eye Detection using Machine Learning Models Nihal Bhandary1, Anish Adnani2 1,2Student,

Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract— Cataract is a clouding of the lens in the eye which impairs vision. The existing systems are trained on a smaller dataset, and have a problem of overfitting. The proposed model is designed to use neural network models to classify a healthy eye and an eye with cataract. With the help of the Convolutional neural network model, cataract is detected. The CNN model has 34 layers. The image goes through successive layers of convolution and pooling. The output is then obtained accordingly at the last layer. The proposed system uses a deep neural network model for the detection of cataract in an eye image.

Keywords — Neural networks, cataract, computer vision, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network).

1. INTRODUCTION Cataracts is a very prevalent eye disease, affecting over 65.2 million people worldwide. The clouding of the lens in an eye obscures or even permanently blocks the light to go through the lens. As a result, the image projected on the retina is blurred and the optic nerves are unable to transmit the image to the brain, leading to blindness.

Fig. 1: Cataract eye visualisation Source: [10] Fig 1 shows a cataract eye. The clouding of the lens blocks the light to pass through the lens resulting in partial or complete blindness depending upon the cataract formation on the lens.

According to the National Blindness and Visual Impairment Survey India 2015-19, cataract is the leading cause of blindness in people above 50 years. Cataract has caused 66.2% blindness cases, 80.7% severe visual impairment cases and 70.2% moderate visual impairment cases in the age group.

The World Vision Report released by the World Health Organization (WHO) on October 8, 2019 also pointed out that high costs involved in accessing eye care, especially, for rural populations was a major driver of visual impairment. The burden of most eye conditions and vision impairment is not borne equally.

Inadequate access to eye care is a major cause of the uneven distribution [12]. The report called for expansion of Universal Healthcare Coverage and including eye care services in it.

In 1976, India launched the National Programme for Control of Blindness to reduce blindness prevalence to 0.3 per cent by 2020.

But, according to the survey released on October 10 2019, the estimated prevalence of blindness still stands at 1.99 per cent, severe visual impairment at 1.96 per cent, moderate visual impairment at 9.81 per cent and moderate severe visual impairment at 11.77 per cent.

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The proposed system can make the task of classifying cataracts more easy and can be operated by layperson. Ophthalmologists can apply operations to different categories of cataracts within a shorter time to cure patients with cataracts. This project will help in

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Volume: 07 Issue: 09 | Sep 2020

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providing access to identify the cataract diseases quickly and with more efficiency.

Classification also differs among previous systems. Few of them have classified the images into two types or three sub-types.

2. LITERATURE SURVEY

3. PROPOSED METHODOLOGY

The study of using machine learning on detection of eye diseases is quite extensive. We found that although the models yielded good accuracy they lacked the proper number of training datasets. The existing systems made use of slit lamp images or fundus images. There has been less effort towards developing a system which can be easily used by the masses.

The proposed system is used to detect the cataract disease using the convolutional neural network model and regular eye images.

3.1. System architecture The detection model is depicted in fig.2 to define the flow of the proposed system.

As discussed in the study [3,8], it used only a few hundreds of images of fundus images which requires proper equipment. The fundus images are the back side of the image. Thus having a pitfall that a naive user cannot benefit from the system. The system [7] classifies images using RNN algorithm and employs features extracted from 5378 images for detection. Few more studies [2,9] classified the eye diseases into 3 types by using Single Perceptron Training Model. The system [3] obtained accuracy of 90.82%. This paper based on the data has produced the said accuracy, the issue faced is limited data and the possibility of overfitting.

Fig.3: System architecture of the proposed system The patient completes the registration process and the data is saved in the system. The registration process obtains patient's demographic information such as age, lifestyle, habits, smoking/alcohol, family history of cataract, had an eye injury or surgery, medications like steroids being used and other diseases. After the user is successfully authenticated, then the user will be transmitted to the home page which would have general information about various eye diseases and how to prevent them along with the symptoms of each eye disease.

Fig. 2: Comparison of existing models and proposed model

Using the ResNet-152 model incorporated in the system any end user can check for the cataract in their eye. All he/she has to do is to click an image of the eye and upload it to the server. Then using the trained model the image will be processed and the results are calculated. If the possibility of cataract is found to be appropriately high, then he would be advised to visit the nearest ophthalmologist.

In fig 2 we see that in most of the existing systems, slit lamp images or fundus images were used for detection. However, as mentioned earlier this might not be feasible to obtain in remote underdeveloped parts of the world. Perceptron models, SVM, BPNN, SVR, RNN, SMO, etc. are used in the previous systems.

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Cloud database is used to store the personal information as well as the results from the CNN model.

3.2.3. Segmentation Pupils need to be detected and extracted as a first step. This is done by using the OpenCV module in python. Hough Circles are used in the process of segmentation. Hough Circles detect circles from the image. The pupils are detected and extracted then they are sent for further processing.

3.2. METHODOLOGY The procedure involves various processes before a result is shown. Sub Processes involved in detecting cataract disease are depicted in modular diagram as shown in fig.6 3.2.1. Load Image The user uploads the eye image to the server for detection. The image must be taken with JPG extension and will be resized according to requirements of the model.

Fig. 5: HoughCircles algorithm Source: [4] As represented in Fig. 4, shows the stages in detection of pupils from an eye image using Hough Circles.

Fig. 4 Modular Diagram

3.2.4. Processed image into the trained model

3.2.2. Preprocessing Preprocessing is used for reshaping and moulding the input data so that it can be fed into the CNN model. Noise from the image is removed or reduced to acceptable levels.

Certain features are enhanced. Finally, the image is grey-scaled.

Once the system obtains a preprocessed image it is then fed into the model where it passes through several layers of the model.

Grayscaling

After the image is found to have cataract, the proposed system tries to further classify it into the subtypes of cataract.

3.2.5. Classification

Compared to a RGB image, grey-scaled images have less dimensions and contain less data. For quicker and efficient processing of images, a grey-scaled image is therefore more fruitful for the model. Hence, the gray scaling method is applied. The reason for using gray scaling is that it retains the important features of the image and reduces the computational load on the CPU because compared to RGB images B/W images take up less space.

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The types of cataract are: 1.

nuclear sclerotic

2.

cortical

3.

posterior subcapsular

The ResNet-152 is a convolutional neural network model with 34 different layers. It is considered to be one of the most accurate vision

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model architecture till date. It has been used in several real-time health-care applications.

3.3 TheResNet Model Recently proposed residual networks (ResNets) get state-of-the-art performance on the ILSVRC2015 classification task and allow training of extremely deep networks upto more than 1000 layers [11]. The ResNet model is used for classification of images. It consists of multiple 3×3 kernel-sized filters one after another. This is in contrast to the AlexNet with large kernel-sized filters (11 and 5 in the first and second convolutional layer, respectively).

The reason for using the ResNet model is that AlexNet and VGG models’ layers might be massive and prone to overfitting. The problem is greatly reduced in the ResNet model without going deep in layers. A dense layer represents a matrix vector multiplication of the weights and inputs from the previous layer. The values in the matrix are the temporary parameters which get updated during backpropagation.

Fig.7: ResNet Architecture [11] Fig 4 represents the architecture of the ResNet model. The diagram displays the number of filters and size of inputs of each layer. There are 64 filter layers in 1st Convolution layer, 128 filter layers in 2nd Convolution layer, 256 filter layers in 3rd Convolution layer and 512 layers in 4th and 5th Convolution layers.

4. IMPLEMENTATION The Convolutional Neural Network model is written in python3 and implemented in jupyter notebooks. We used CPU instead of GPU for processing. Tensorflow, keras and numpy modules were used in making the model.

Initially, the weights were randomized. During the course of the training, the weights were being successively changed to the final values.

The images were obtained from the open sources available for research use on kaggle.com.

Fig. 6: ResNet layer Source: [11] Fig 3 represents different layers present in the ResNet-152 model. This model consists of convolutional layers, Max Pooling layers, Activation layers, Fully connected layers. The convolutional layer is made of 3x3 size whereas the Max Pooling layer is made of 2x2 sized filters.

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5. RESULT AND ANALYSIS Initially, we used 4-5 alternate layers of convolution and pooling. The accuracy was close to 0.52 with loss upto 7.35. The ResNet-152 model with 34 layers of convolution and pooling with pre-trained weights were then employed.

The accuracy came around 0.7 and loss in the order of e-4 on validation data. By varying epochs in between 4 and 30, we found the accuracy of model peaked at 7 epochs.

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Fig. 10: Data being labelled by the model as cataract or non-cataract The figure 10 shows the output from the model, where the eye images are detected either as cataract or non-cataract. The figure 11 shows the type and weights of the layer in the final model.

Fig 8: Model Accuracy

Fig 9: Model Loss Figure 8 and 9 represent the model accuracy and model loss graphs, the bifurcation of train and test is represented in the same figure.

Fig. 11: Weight parameters in each layer of model 6. CONCLUSION A convolutional neural network model is thus developed using weights obtained by training the model on a large dataset of cataract and noncataract eye images.

7. REFERENCES [1] Xingzhi Qian, Evan W. Patton, Justin Swaney, Qian Xing, Tingying(Helen) Zeng, “Machine Learning on Cataracts Classification Using SqueezeNet“ for IEEE 4th International Conference on Universal Village • UV2018 • Session 2-4.

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[2] Riyanto Sigit, Maulana Bayu Satmoko, Dwi Kurnia Basuki, “Classification of Cataract Slitlamp Image Based on Machine Learning “ for 2018 International Seminar on Application for Technology of Information and Communication (iSemantic) [3] Yanyan Dong, Qinyan Zhang, Zhiqiang Qiao, Ji-Jiang Yang, “Classification of Cataract Fundus Image Based on Deep Learning” for IEEE Instrumentation and Measurement Society.

[4] Wang Xueguang, Du Xiaowei, “The application of SVM in automatic Eye Position Algorithm” for 2009 2nd International Conference on ICT. [5] Su Yeong Gwon1, Chul Woo Cho1, Hyeon Chang Lee1, Won Oh Lee1 and Kang Ryoung Park2,”Robust Eye and Pupil Detection Method for Gaze Tracking '' for International Journal of Advanced Robotic Systems.

[6] Sergey loffe, “RED EYE DETECTION WITH MACHINE LEARNING” for FUJIFILM Software (Califomia).

[7] Xinting Gao∗, Member, IEEE, Stephen Lin, Member, IEEE, and Tien Yin Wong, “Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning” for IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62 [8] Narit Hnoohom, Anuchit Jitpattanakul, “Comparison of Ensemble Learning Algorithms for Cataract Detection from Fundus Images“ for 2017 International Computer Science and Engineering Conference [9] Patwari, Professor & Arif, Muammer & Chowdhury, Md Nurul & Arefin, A. & Imam, Md. Ikhwanul. (2011). “Detection, Categorization, and Assessment of Eye Cataracts Using Digital Image Processing.” for The First International Conference on Interdisciplinary Research and Development.

[10] Niya C P, “Automatic cataract detection and classification systems: A survey” for TechS Vidya e-Journal of Research ISSN 2322 - 0791 Vol. 3 (2014-15) pp. 28-3 [11] Resnet in Resnet: Generalizing Residual Architectures by Sasha Targ, Diogo Almeida, Kevin Lyman.

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