Computer Vision-Based Deep Learning Techniques for Diabetic Retinopathy Assessment from Fundus Images

Abstract

Long-term diabetes can damage the blood vessels in the retina, resulting in diabetic retinopathy (DR), a form of vision impairment. It affects around 191 million people worldwide and is the most prevalent cause of blindness. Although earlier studies have focused on DR categorization by retinalfundus pictures, current techniques typically concentrate on identifying certain tumors without providing a thorough basis for concurrently detecting each lesion. Prior research focused on earlystage Features such as blood vessels, hemorrhages, aneurysms, and exudates highlight lesions at the severe stage.such as exceedingly severe intraretinal microvascular abnormalities (IRMA), venous beading, and cotton wool patches,Retinal pigment, capillary degeneration, diffuse intraretinal hemorrhages, and extremely active microglia impairment of RPE. This work suggests that deep learning can be used to classify DR fundus images.different degrees of severity, using adaptive particle swarm optimizer-based GoogleNet and ResNet models (APSO) to improve feature extraction. After then, the hybrid model's features are put to use.employing a range of machine learning methods, such as decision trees, random forests, support vector machines, and models of linear regression. According to experimental results, the suggested hybrid framework performed better than sophisticated methods that achieved an astounding 94% accuracy rate on the benchmark dataset. This approach illustrates possible improvements in F1 score, recall, accuracy, and precision for varying degrees of DR severity.