Multi-Feature Extraction with Ensemble Network for Tracing Chronic Retinal Disorders

The retina manifests a vital role in tracing chronic retinal disorders. It is located near the optic nerve that transforms the captured light into neural signals. The most prominent chronic eye diseases exhibit themselves in the retina. The analysis of a retina for detecting disease symptoms is quite challenging. Most of the prior methods developed using shallow and deep learning algorithms primarily emphasized single feature extraction for disease diagnosis. The underlying article has designed an ensemble network for extracting multiple retinal features using a single comprehensive platform. It includes a set of models that reflect feature-based needs to prevent intensity loss, micro-vessels overlap, and data redundancy. The proposed method has experimented with prominent benchmark datasets developed for vessels tree, optic disc/cup, and arteries/veins extraction. It is also compared with other methods and achieved promising results. Our platform is helpful for physicians to trace the variations in the retina of subjects facing chronic retinal disorders.