Multiple Ocular Disease Detection Using a Generic Explainable Diagnostic Framework

Abstract

Age-related Macular Degeneration (AMD), Cataract, Diabetic Retinopathy (DR) and Glaucoma are the four most common ocular conditions that affect a person s vision. Early detection in the asymptomatic stages can slow down the progression of these diseases and alleviate vision loss. However, manual diagnosis is costly, tedious, laborious and burdensome, especially in mass screening applications. Computer aided diagnosis (CAD) systems serve as an aid to ophthalmologists in the efficient diagnosis of ocular diseases. Many existing CAD systems for ocular diseases primarily address a single disease condition, employing disease-specific algorithms that rely on anatomical and morphological characteristics for localization of regions of interest (ROIs). The dependence on exhaustive image processing algorithms for pre-processing, ROI detection and feature extraction often results in overly complex systems prone to errors that affect the classifier performance. Conglomerating many such individual diagnostic frameworks, each targeting a single disease, is not a practical solution for detecting multiple ocular diseases, especially in mass screening. Alternatively, a single generic CAD framework modelled as a multiclass problem serves to be useful in such high throughput scenarios, significantly reducing cost, time and manpower. Nevertheless, ambiguities in the overlapping features of multiple classes representing different diseases should be effectively addressed. This thesis focuses on a generic CAD system for the diagnosis of AMD, cataract, DR and glaucoma, which could serve as a boon in a large-scale screening scenario by reducing the time and manpower involved. The proposed schemes rely on segmentation-independent approach that eliminates the need for individual diagnostic systems each involving a set of disease-specific algorithms for localisation and analysis of ROIs. This approach also alleviates segmentation inaccuracies attributed to image quality and anatomical factors, which have a cascaded effect....