Cataract Detection and Classification Using Deep Learning Techniques

Abstract

Detecting eye diseases in the early part can reduce the damage to the eye and get the cure. Using artificial intelligence techniques in medical applications, it can detect and classify eye diseases using deep learning models with color images of the eye. In this paper cataract detection, recognition, and classification have been achieved using Convolutional Neural Network (CNN) deep-learning models applied to retinal fundus color images. A sample of 400 color images dataset has been classified into 300 normal images and 100 cataract images. These datasets were pre-processed automatically using histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in addition to the segmentation process. There are three models have been used in this work, GoogleNet, ResNet-101, and Densenet201, they are applied in three cases, the first case uses the original images without image preprocess, the second with HE images pre-process, and the third with HE and CLAHE pre-processed images and achieved high testing accuracy exceeding 98% with Densenet201 model and achieved classification accuracy of 90% with GoogleNet model. The experimental results are evaluated using common performance metrics such as accuracy, precision, sensitivity, specificity, and F1-score for both cataract detection and classification cases. The performance of the proposed work makes this model can be used to improve eye health, including accuracy, early detection, training, and future education, and a considerable step toward the automatic detection and classification of cataract efficacy procedures for assisting detection and performing.