A New Model for Retinal Lesion Detection of Diabetic Retinopathy Using Hierarchical Self-Organizing Maps

Diabetes is a disease that impairs blood flow throughout the body. In this disease, the retinal blood vessels may leak and cause retinal swelling known as edema. The person’s sight might be affected if this swelling happens in the central vision area of retina, the macula. In this paper, we proposed a classification system, including a novel combination of Self-Organizing Maps (SOM) for detecting retinal lesions. The proposed system consists of a fast pre-processing step followed by lesion feature extraction and, finally, a detailed classification model. In the pre-processing stage, the system is divided into the three procedures of initial target lesion extraction, optical disk extraction, and eventually extracting retinal blood vessels from the retina. The second step is a combination of multiple features such as morphology, color, intensity, and moments. The classifier is a model of Hierarchical Self-Organizing Maps (HSOM), which aims to increase the accuracy and speed of classifying the lesions while considering the high amount of data in extracting the features. The overall accuracy and sensitivity of the proposed method according to the MESSIDOR database is 97.87% and 98.51%, respectively. The results show that the proposed model can detect and classify the Lesions in HDR images accurately.

Retinopathy Self-organizing neural networks Classification Retinal lesions