Diabetic Retinopathy Classification using Deep Learning
Channel: British Computer Society Open Source Specialists
Date: 18/06/2021

Presented by Mohammad T. Al-Antary

Diabetic Retinopathy (DR) is a highly prevalent complication of diabetes mellitus, which causes lesions on the retina that affect vision which may lead to blindness if it
is not detected and diagnosed early. Convolutional neural networks (CNN) are becoming the state-of-the-art approach for automatic detection of DR by using fundus images. The high-level features extracted by CNN are mostly utilised for the detection and classification of lesions on the retina. This high-level representation is capable of classifying different DR classes; however, more effective features for detecting the
damages are needed. This research work proposes the multi-scale attention network (MSA-Net) for DR classification. The proposed approach applies the encoder network
to embed the retina image in a high-level representational space, where the combination of mid and high-level features is used to enrich the representation. Then a multi-scale feature pyramid is included to describe the retinal structure in a different locality. Furthermore, to enhance the discriminative power of the feature representation a multi-scale attention mechanism is used on top of the high-level
representation. The model is trained in a standard way using the cross-entropy loss to classify the DR severity level. In parallel as an auxiliary task, the model is trained using
the weakly annotated data to detect healthy and non-healthy retina images. This surrogate task helps the model to enrich its discriminative power for distinguishing the non-healthy retina images. The proposed method when implemented has achieved outstanding results on two public datasets: EyePACS and APTOS.

Mohammad T. Al-Antary received the B.Sc. degree in computer information systems from The University of Jordan, Amman, Jordan, in 2015, and the M.Sc. degree in enterprise systems and
database administration/computing and information systems from the University of Greenwich, U.K., in 2017, where he is currently pursuing the Ph.D. degree in computing and information systems with the School of Computing and Mathematical Sciences. He is currently working as a Teaching Assistant with the School of Computing and Mathematical Sciences, University of Greenwich. His research and professional interests include data science, medical image processing, machine learning, business intelligence, and big data.