A comprehensive study for predicting eyesight disease using ML

Abstract

From mild to severe distant vision impairment caused by untreated conditions, including cataract, glaucoma, retinal disease, and diabetic retinopathy, more than60% of the world’s population—exceeding 4.5 billion individuals—requires corrective lenses or treatments for visual and retinal disorders. The fundamental goal ofthe current study is to create an advanced deep learning (DL) system capable ofcategorizing retinal pictures into five groups. A deep convolutional neural network(CNN) was used to classify normal eyes, cataracts, glaucoma, retinal illness, and diabetic retinopathy. The dataset, obtained from Kaggle, had 2827 pictures that wererandomly divided into training, validation, and testing groups. The TensorFlowobject identification framework was used to create many CNN meta-architectures,including YOLOv5, YOLOv7, and InceptionResNet50. The YOLOv5 model showedgreat development. The YOLOv5 model demonstrated significant progress in detecting the mentioned eye diseases and achieving 0.951 mAP for 7357 images.