# An Attention Infused Deep Learning System with Grad-CAM Visualization for Early Screening of Glaucoma

arXiv: https://arxiv.org/abs/2505.17808 · HTML: https://mlsanity.com/paper/2505.17808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 5 of 5)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Attention Based Glaucoma Detection: A Large-scale Database and CNN Model](https://mlsanity.com/paper/1903.10831/) (2019)
- [Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review](https://mlsanity.com/paper/2204.05591/) (2022)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)

## Abstract (arXiv)

This research work reveals the strengths of intertwining a deep custom convolutional neural network with a disruptive Vision Transformer, both fused together with a radical Cross-Attention module. Here, two high-yielding datasets for artificial intelligence models in detecting glaucoma, namely ACRIMA and Drishti, are utilized. The Cross-Attention mechanism facilitates the model in learning regions in the fundus that are clinically relevant through bidirectional feature exchange between CNN and ViT streams. Experiments clearly depict improved performance when compared to standalone baseline CNN and ViT models.