HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

Abstract

Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impair-ment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge the model’s width by substituting it with primary convolutional layers. Then, aggregation blocks were used to deepen the model to extract more compact and discriminating features. Finally, the downsampling blocks were adopted to reduce the feature map size to decrease the learning time, and the upsam-pling blocks were used to resize the feature maps. This methodology ensured high prominence to lesion regions compared to the non-lesion regions. The performance of the proposed model was assessed on two benchmark compet-itive datasets called Asia Pacific Tele-Ophthalmology Society (APTOS) and KAGGLE, attaining accuracy rates of 95% and 92%, respectively.