RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework

• URL: http://arxiv.org/abs/2602.19324v1
• Date: Sun, 22 Feb 2026 20:05:54 GMT
• Title: RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework
• Authors: Mohammad Tahmid Noor, Shayan Abrar, Jannatul Adan Mahi, Md Parvez Mia, Asaduzzaman Hridoy, Samanta Ghosh,
• Abstract summary: We proposed a deep learning method for retinal disease classification utilizing optical coherence tomography ( OCT) images.<n>Xception was the most accurate network (95.25%) followed closely by InceptionV3 (94.82%)<n>These results suggest that deep learning methods allow effective retinal disease classification and highlight the importance of accuracy and interpretability for clinical applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early and accurate classification of retinal diseases is critical to counter vision loss and for guiding clinical management of retinal diseases. In this study, we proposed a deep learning method for retinal disease classification utilizing optical coherence tomography (OCT) images from the Retinal OCT Image Classification - C8 dataset (comprising 24,000 labeled images spanning eight conditions). Images were resized to 224x224 px and tested on convolutional neural network (CNN) architectures: Xception and InceptionV3. Data augmentation techniques (CutMix, MixUp) were employed to enhance model generalization. Additionally, we applied GradCAM and LIME for interpretability evaluation. We implemented this in a real-world scenario via our web application named RetinaVision. This study found that Xception was the most accurate network (95.25%), followed closely by InceptionV3 (94.82%). These results suggest that deep learning methods allow effective OCT retinal disease classification and highlight the importance of implementing accuracy and interpretability for clinical applications.

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