Fuzzy Image Processing and Deep Learning for Microaneurysms Detection

Abstract

Diabetic retinopathy is an eye disease generated by long-standing diabetes, and it is one of the main causes of vision loss if not diagnosed and treated properly. Diabetic retinopathy consists of several types of lesions found in the retina of diabetic individuals. One of the important lesions of diabetic retinopathy is microaneurysms, which are small red dots that appear due to the local weakness of the capillary walls. This paper presents a novel automatic microaneurysms detection method, in retinal images by employing fuzzy image processing and deep learning. Firstly, the paper explores the existing systems of diabetic retinopathy screening, with a focus on the microaneurysms detection methods and deep learning classification. The proposed system consists of two parts, namely: image preprocessing with a combination of fuzzy image processing techniques, and also the microaneurysms classification using deep neural networks. This paper investigates the capability of a combination of different fuzzy image preprocessing techniques for the detection of microaneurysms in eye fundus images. In addition to the proposed microaneurysms detection system, the paper also highlights a novel dataset for the microaneurysms detection that includes the ground truth data. The purpose of the proposed automated microaneurysm detection with digital analysis of eye fundus images is to substitute current practice that is based on manual diagnosis and visual inspection, and eventually to contribute to producing a more reliable diabetic retinopathy screening system.