-5171ffc0182b6af927f-1c53 20221114065814604 ijaas:ijaas ch.ta.yeh@gmail.com WEB-FORM International Journal of ADVANCED AND APPLIED SCIENCES Int. j. adv. appl. sci. 2313626X 23133724 12 2022 12 2022 9 12 Department of CSE, Gitam School of Technology, Gitam Deemed to be University, Visakhapatnam, India Gorli L. Aruna Kumari Poosapati Padmaja Department of IT, Anil Neerukonda Institute of Technology and Science, Visakhapatnam, India Jaya G. Suma Department of IT, College of Engineering, Jawaharlal Nehru Technological University, Kakinada, India Diabetes is the most prevalent condition worldwide, and diabetic retinopathy (DR) is a subsequent condition caused by acute diabetic cases. It causes severe degeneration of the retina. The compounding blood vessels bloat and often burst, causing fluid leaks in the aqueous humor. This, in turn, causes the creation of undesirable nerve fiber infractions from the occlusion of arteries. Diagnosis requires a manual retinal examination that can often be inconsistent and deliberate with potential flaws in the diagnosis. Early detection through an ophthalmologist is paramount to prevent the prognosis of severe vision loss. Considering the current leap of machine learning in the field of healthcare, early detection of DR can be potentially made efficient with intelligent systems. This research proposes methodologies to fine-tune the existing pre-trained architectures, attaining the classification accuracies of 98% to classify the ocular fundus images which identify early prediction of diabetes. Additionally, this study presents an exposition of other equally scrutinized approaches to ultimately showcase a deep neural network architecture that can precisely classify normal fundus and degenerated fundus from the lowest to the most severe hierarchy. Among several layers in the CNN model pre-tuning and post-tuning exception layers outperformed with good results. 12 2022 12 2022 135 144 10.21833/ijaas-crossmark www.science-gate.com true IASE www.science-gate.com 10.21833/ijaas.2022.12.017 http://www.science-gate.com/IJAAS/2022/V9I12/1021833ijaas202212017.html http://science-gate.com/IJAAS/Articles/2022/2022-9-12/1021833ijaas202212017.pdf