An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm

Abstract

Recently, the leading cause of preventable blindness is diabetic retinopathy (DR). Although there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal screening could facilitate the early detection and treatment of DR. The goal of this research is to develop a reliable DR screening and detection model to reduce the risk of DR-related blindness. DR-infected eyes describe ophthalmologist for further examination and diagnosis might reduce the risk of vision loss and provide timely and accurate diagnostic information. Hence, this paper proposes a hybrid inductive machine learning algorithm (HIMLA) as an automated DR detection diagnostic tool. HIMLA processes and classifies colored fundus images as healthy (no retinopathy) or unhealthy (presence of DR) by identifying the appropriate medical DR cases. The proposed algorithm comprises four stages: pre-processing, segmentation, feature extraction, and classification. At the pre-processing stage, colored fundus images are normalized to a specific brightness level to enhance the quality of the images. In the segmentation stage, the processed image is encoded and decoded to segment the images for improving image quality. Furthermore, feature extraction and classification are performed using multiple instance learning (MIL). The proposed method was evaluated on CHASE datasets for the detection of DR. The accuracy, sensitivity, and specificity of the proposed approach are 96.62%, 95.31%, and 96.88%, respectively. These results indicates that HIMLA outperforms other DR models, such as ML-based neovascularization detection in the optic disc (MLB-NVD), genetic algorithm–based diabetic retinopathy (GAB-DR), DL algorithm diabetic retinopathy (DLA-DR), and diagnostic assessment–based DL for diabetic retinopathy (DAD-DR ), which reduces the risk of vision loss.