Comprehensive Eye Disease Classification Using Deep Learning

Abstract

The domain of ophthalmology has undergone significant progress with the incorporation of automated diagnostic methods, particularly via deep learning. The precise and timely identification of prevalent ocular disorders, including diabetic retinopathy, glaucoma, and cataracts, is essential, as these ailments, if neglected, may result in significant visual impairment or complete blindness. Conventional diagnostic techniques, which depend significantly on manual assessment and specialist interpretation, can be laborious and susceptible to inconsistency. Conversely, automated deep learning methodologies present a viable alternative, delivering more consistent and expedited diagnoses.

This study aims to utilise deep learning models, specifically Convolutional Neural Networks (CNNs), DenseNet121, and Xception, to improve the automated classification of ocular illnesses. Convolutional Neural Networks (CNNs) are esteemed for their capacity to discern spatial hierarchies in images, rendering them appropriate for medical image analysis. DenseNet121, characterised by its dense connections, and Xception, recognised for its depth wise separable convolutions, are sophisticated designs capable of extracting complex characteristics from retinal images. In conjunction with these deep learning methodologies, conventional machine learning classifiers such as Random Forest and Support Vector Machine (SVM) are utilised to compare and maybe amalgamate their outputs, with the objective of improving diagnostic precision.

The principal objective of this work is to create a dependable, automated system for the classification of eye diseases. A thorough examination of pertinent literature is performed to comprehend current approaches, pinpoint deficiencies, and delineate the issues encountered in this field. Critical concerns encompass the necessity for extensive annotated datasets, the mitigation of class imbalances, and the reduction of false positives. Through the examination of these problems, we suggest several solutions, including data augmentation, transfer learning, and ensemble modelling. The research closes by proposing hypotheses to validate the efficacy and resilience of the suggested models, thereby boosting diagnostic tools in ophthalmology, alleviating the workload of healthcare workers, and improving patient outcomes