| Summary: | Diabetic retinopathy (DR) and Glaucoma are two major ocular diseases that lead to vision impairment if not detected and treated promptly. Manual detection and diagnosis of these diseases is a laborious and time-consuming process. Computer aided diagnosis (CAD) systems can serve to assist physicians in manual diagnosis. This work aims to develop a generic multiocular CAD system for DR and glaucoma diagnosis, which could serve as a boon in a large scale screening scenario by reducing the time and manpower involved. To this end, a segmentation-independent approach is employed that eliminates the need for individual diagnostic systems each involving a set of disease-specific algorithms for localisation and analysis of regions of interest (ROIs). Further, the proposed approach alleviates the segmentation inaccuracies attributed to image quality and anatomical factors, which have a cascaded effect on the classification performance. Specifically, a machine learning (ML) model based on random forest (RF) classifier and a pool of non-linear features including higher order spectra (HOS), entropy and fractal features, was developed. An ensemble of this ML model and convolutional neural network (CNN)-based deep learning (DL) model was further constructed using the ‘sum rule’ for decision fusion. The proposed ensemble model resulted in accuracy, sensitivity and specificity as high as 98.08%, 98.37% and 99.07% respectively, for three-class classification to categorize samples as normal, DR or glaucoma.
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