Enhancing Cataract Detection through Hybrid CNN Approach and Image Quadration: A Solution for Precise Diagnosis and Improved Patient Care

Abstract

Cataracts, characterized by lens opacity, pose a significant global health concern, leading to blurred vision and potential blindness. Timely detection is crucial, particularly in regions with a shortage of ophthalmologists, where manual diagnosis is time-consuming. While deep learning and convolutional neural networks (CNNs) offer promising solutions, existing models often struggle with diverse datasets. This study introduces a hybrid CNN approach, training on both full retinal fundus images and quadrated parts (i.e., the fundus images divided into four segments). Majority voting is utilized to enhance accuracy, resulting in a superior performance of 97.12%, representing a 1.44% improvement. The hybrid model facilitates early cataract detection, aiding in preventing vision impairment. Integrated into applications, it supports ophthalmologists by providing rapid, cost-efficient predictions. Beyond cataract detection, this research addresses broader computer vision challenges, contributing to various applications. In conclusion, our proposed approach, combining CNNs and image quadration enhances cataract detection’s accuracy, robustness, and generalization. This innovation holds promise for improving patient care and aiding ophthalmologists in precise cataract diagnosis.