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Diabetic retinopathy (DR) syndrome affects the vision of the eyes by damaging the blood vessels. Fore-hand detection and prevention of this syndrome are most significant as it results in vision blindness. Diagnosis and procedural analysis of this syndrome with modern healthcare science and technology are aided through artificial intelligence and processing units. In this article, a threshold segmentation based DR detection method is introduced. This method is keen is classifying the foreground and background of the input retinal image and processing through pixel-based segmentation. The process of assessing the layers is augmented using a two-layer convolutional neural network (CNN) that mitigates the false positives during classification. This process is sequential in determining the precise detection of the infected region of the retina. Besides, the segment-based CNN (S-CNN) handles the flaw in diagnosis through two-hidden layers for differentiating the threshold and normalized conditions based on classification. The proposed method is reliable in achieving better accuracy of detection, sensitivity, and true positives.

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