Diabetic Retinopathy Detection through Multiclass Classification of Fundus Image Using Convolutional Neural Network

Abstract

Diabetic retinopathy (DR) is an eye disease which is caused by high blood sugar and high blood pressure and damage the blood vessels in the back (retina) of the eye. Diabetic retinopathy (DR) is not a reversible process and treatment only sustain vision. The number of ophthalmologists cannot meet the growing demands around the world. This study focuses on the automatic diagnosis of the disease through deep learning. Convolutional neural network (CNN) is more widely used as a deep learning method in medical image analysis. Multiclass image classification of images into non referable DR and referable DR has been done using proposed Convolutional neural network (CNN) model. For multiclass classification problem, the sensitivity, specificity and F2 score value for class 0 (no DR) are 81.75 %, 91.06 % and 81.80 % respectively. Whereas for class 1 (non-severe DR), sensitivity, specificity and F2 score values are 71.28 %, 78.52 % and 70.01 % respectively. Similarly for class 2 (sever DR), sensitivity, specificity and F2 score values are 73.03 %, 93.39 % and 75.08 % respectively. Bayesian optimization has been performed for tuning learning rate and gives optimal learning rate 0.000358 through the optimization process. The customized CNN is then trained using 0.000358 learning rate and then tested on test data The images in dataset have poor contrast and consists of impulse noises. Contrast limited adaptive histogram equalization (CLAHE) method is used to improve the contrast of the image followed by median filter to remove noise present in DR image.