A major global cause of blindness, age-related macular degeneration (AMD) is characterized by progressive vision loss and frequently coexists with choroidal neovascularization (CNV), an aberrant development of blood vessels. Medical problems require the knowledge of physicians, but because neural networks are opaque, machine learning techniques have not been well received in the medical community. However, these techniques may be useful in the diagnosis of retinal disorders. Our research employs Deep Neural Network (DNN) technology to classify CNV from Optical Coherence Tomography (OCT) pictures in a unique manner. For timely intervention and treatment, we used well-tuned MobileNet and VGG19 models to guarantee precise CNV identification. Acknowledging reluctance in the medical com-munity toward machine learning, we tackled the need for accurate and comprehensible diagnosis. Through careful preprocessing of the data and the use of transfer learning on Convolutional Neural Network (CNN) models, such as VGG 19 and MobileNet, our methodology produced remarkable validation accuracies of 99.89% and 99.3% per respective. Our findings highlight the ability of improved deep neural networks to increase classification accuracy while simultaneously lowering processing demands. By aiding in the early detection and management of serious retinal disorders, this research advances reliable diagnostic instruments.

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