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Blindness detection in diabetic retinopathy using Bayesian variant-based connected component algorithm in Keras and TensorFlow

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The neuro-degenerative eye disease glaucoma is caused by an increase in eye pressure inside the retina. As the second- leading cause of blindness in the world, if an early diagnosis is not obtained, this can cause total blindness. Regarding this fundamental problem, there is a huge need to create a system that can function well without a lot of equipment, highly qualified medical personnel, and takes less time. The proposed modeling consists of three stages: pre-training, fine-tuning and inference. The probabilistic based pixel identification (Bayesian variant) predicts the severity of Diabetic Retinopathy (DR) which is diagnosed by the presence of visual cues, such as abnormal blood vessels, hard exudates, and cotton wool spots. The article combines machine learning, deep learning, and methods for image processing to predict the diagnosis images. The input picture is validated using Bayesian variant connected component architecture, and the brightest spot algorithm is applied to detect the Region of Interest (ROI). Moreover, the training sample calculated optic disc and optic cup are segmented with fundus photography ranges 0 to 4 using VGGNet16 architecture and SMOTE algorithm to detect DR stages of images and the proposed model using ensemble based ResNet with Efficient Net produces the excellent accuracy score of 93 % and predicted image Kappa coefficient (p < 0.01) 0.755 of the fundus retina image dataset.

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