Intelligent glaucoma classification models for fundus images

Abstract

Glaucoma, the most important cause of irrevocable blindness, can be newlineaverted or retain the progression of the disease if detected early and managed newlineproperly. Developing an automatic glaucoma detection system is an important newlineresearch topic because manual screening programs for the mass population for newlineearly detection and diagnosis of the disease are not feasible. Diagnosis of newlineGlaucoma is detected through medical image analysis. Deep learning has newlineadvanced significantly in many other real-time applications, and it is now newlinecontributing significantly to better performance for medical image analysis, and newlineit is regarded as an important technique for the medical sector in the future. newlineCurrent Glaucoma image analysis approaches automatically provide a feature newlineextraction process without exploiting deep learning. This research examined newlinedeep learning based techniques to aid early Glaucoma detection and address newlinepresent issues. newlineThe literature on Glaucoma classification using deep learning newlineperformed by various authors is analyzed and summarized. The various deep newlinelearning models proposed by researchers in this field are carefully observed and newlineinvestigated. Following a thorough review of the literature, the EfficientNet newlinemodel was chosen as the method of Glaucoma classification research in this newlinethesis, and selects similar deep CNN such as VGG16, InceptionV3 and newlinedeveloped the new 15 layers CNN as the comparison model to carry out the newlinebinary and three-class classification of Glaucoma. newlineThe research aims to develop an intelligent Glaucoma grading system newlineto diagnose an early and advanced stage of Glaucoma that causes major vision newlineloss. newline