Deep Learning Grading System for Diabetic Retinopathy using Fundus Images. (Dept. E)

Abstract

Diabetic Retinopathy (DR) is one of the main causes of blindness that can be overcome, if it is early detected. This work proposes an automated early detection and grading of DR using fundus images. The proposed detection and grading system investigate different deep learning architectures (i.e., ResNet and AlexNet) that are applied to an augment data to extract deep compact features of the fundus images. The extracted features are input to a pixel-wise Neural Network (NN) classifier or a Support Vector Machine (SVM) classifier for automated DR grading. The performance of the proposed system is evaluated using a publically available fundus Indian Diabetic Retinopathy Image Dataset (IDRiD), collected for ISBI-2018 challenge. The IDRiD dataset consists of 516 retinal images of normal and different DR grades, i.e., mild, moderate, severe, and Proliferative Diabetic Retinopathy (PDR). Our system achieves an overall accuracy of 95.73%, sensitivity of 95.73%, and specificity of 98.51% utilizing an AlexNet-based architecture and a pixel-wise NN classifier. Compared to the previous related work, the proposed system shows promising DR grading performance.

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

“…We applied the two models (Densenet and FCN-Alexnet) in the proposed system, since their decoders produce outputs that are of the same dimensions as the input image, which suits the task of segmentation. In addition, they have shown outstanding performance for several related medical applications, such as lung segmentation [17], [18], pulmonary cancerous detection [19], face recognition [20], brain cancer [21] and diabetic retinopathy [22].…”