An effective and comprehensible method to detect and evaluate retinal damage due to diabetes complications

Abstract

The leading cause of vision loss globally is diabetic retinopathy. Researchers are making great efforts to automatically detect and diagnose correctly diabetic retinopathy. Diabetic retinopathy includes five stages: no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, severe diabetic retinopathy and proliferative diabetic retinopathy. Recent studies have offered several multi-tasking deep learning models to detect and assess the level of diabetic retinopathy. However, the explanation for the assessment of disease severity of these models is limited, and only stops at showing lesions through images. These studies have not explained on what basis the appraisal of disease severity is based. In this article, we present a system for assessing and interpreting the five stages of diabetic retinopathy. The proposed system is built from internal models including a deep learning model that detects lesions and an explanatory model that assesses disease stage. The deep learning model that detects lesions uses the Mask R-CNN deep learning network to specify the location and shape of the lesion and classify the lesion types. This model is a combination of two networks: one used to detect hemorrhagic and exudative lesions, and one used to detect vascular lesions like aneurysm and proliferation. The explanatory model appraises disease severity based on the severity of each type of lesion and the association between types. The severity of the disease will be decided by the model based on the number of lesions, the density and the area of the lesions. The experimental results on real-world datasets show that our proposed method achieves high accuracy of assessing five stages of diabetic retinopathy comparable to existing state-of-the-art methods and is capable of explaining the causes of disease severity.

Keywords

• Diabetic retinopathy
• Deep learning
• Retinal damage detection
• Lesion segmentation
• Fundus image
• Haemorrhages