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

arXiv: https://arxiv.org/abs/2602.19324 · HTML: https://mlsanity.com/paper/2602.19324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods](https://mlsanity.com/paper/2312.08255/) (2023)
- [OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection](https://mlsanity.com/paper/2401.12344/) (2024)
- [Smart Diagnosis and Early Intervention in PCOS: A Deep Learning Approach to Women's Reproductive Health](https://mlsanity.com/paper/2602.04944/) (2026)
- [Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection](https://mlsanity.com/paper/2602.17797/) (2026)

## Abstract (arXiv)

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.