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[2602.19324] RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework
Summary
The article presents RetinaVision, a deep learning framework for accurate classification of retinal diseases using optical coherence tomography images, achieving high accuracy with interpretability features.
Why It Matters
Retinal diseases are a leading cause of vision loss. This research emphasizes the importance of early diagnosis and the use of advanced AI techniques to improve clinical outcomes. By integrating explainable AI, the study also addresses the critical need for transparency in medical AI applications.
Key Takeaways
- RetinaVision employs deep learning for retinal disease classification with high accuracy.
- Xception and InceptionV3 architectures were tested, with Xception achieving 95.25% accuracy.
- Data augmentation techniques were used to enhance model generalization.
- Interpretability methods like GradCAM and LIME were applied for clinical relevance.
- The study highlights the significance of combining accuracy and interpretability in medical AI.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19324 (cs) [Submitted on 22 Feb 2026] 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 View a PDF of the paper titled RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework, by Mohammad Tahmid Noor and 5 other authors View PDF 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 meth...
