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Description
This scientific article describes a project focused on creating a convolutional neural network designed to assist physicians in diagnosing diabetic retinopathy based on Optical Coherence Tomography (OCT) images. The project involves an in-depth analysis of existing research on the classification of this disease using fundus images. Leveraging a diverse dataset of OCT images, encompassing both eyes affected by diabetic retinopathy and healthy eyes, we developed an effective neural network model. The proposed model demonstrates promising potential in supporting medical professionals in the diagnostic process for diabetic retinopathy. This research contributes to the advancement of medical imaging technologies and holds promise for improved healthcare outcomes in the realm of diabetic eye complications.
