International Journal of Advanced Research in Computer and Communication Engineering

Abstract: OCT imaging is an essential test to help diagnose retinal diseases involving Choroidal Neovascularization (CNV), DME, Drusens, etc. They get accurately and automatically classified with the early help of ophthalmologists for diagnosis and treatment. In semi-supervised learning practice, active learning is a popular technique specialized in the selection of training sets. The OCT dataset comprises a total of 83,500 retinal images of high quality. In a similar manner, the dataset assigns an equal quantity of images to all four classes. We resize the images to make them 224 × 224 pixels. Normalize the image and apply some augmentations. Improvements used on the images include flipping, rotating, zooming and changes in the brightness. Improvements assist in reinforcing our model by avoiding overfitting. The adam optimizer is used to train the model while learning specific retina characteristics with the categorical cross-entropy loss function. The suggested approach attained an overall accuracy of 95.2% with all class precision, recall and F1-score being 94%. The Grad-CAM visualisation shows the model focusing correctly on the retina. The study suggested that deep learning techniques or explainable AI can help ophthalmologists diagnose retinal diseases automatically, which can further help in clinical decision making. The statement reveals that the transfer learning models can provide reliable as well as explainable results in retinal diseases.

Keywords: OCT imaging, learning, retinal, overfitting and optimizer.

Downloads: | DOI: 10.17148/IJARCCE.2025.14923