Diabetic retinopathy grading using ResNet convolutional neural network

Sallam, Muhammad Samer and Asnawi, Ani Liza and Olanrewaju, Rashidah Funke (2020) Diabetic retinopathy grading using ResNet convolutional neural network. In: 2020 IEEE Conference on Big Data & Analytics, 17-19 December 2020, Kuala Lumpur (Online Conference).

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Abstract

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (– No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis.

Item Type:	Conference or Workshop Item (Slide Presentation)
Additional Information:	4327/86521
Uncontrolled Keywords:	Convolutional neural networks, retinal fundus images classification, transfer learning, diabetic retinopathy
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	DR. Ani Liza Asnawi
Date Deposited:	30 Dec 2020 08:43
Last Modified:	28 Mar 2021 15:28
URI:	http://irep.iium.edu.my/id/eprint/86521

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