# MaxGlaViT: A novel lightweight vision transformer-based approach for early diagnosis of glaucoma stages from fundus images

arXiv: https://arxiv.org/abs/2502.17154 · HTML: https://mlsanity.com/paper/2502.17154/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [DaViT: Dual Attention Vision Transformers](https://mlsanity.com/paper/2204.03645/) (2022)
- [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://mlsanity.com/paper/2303.14189/) (2023)
- [FlexiViT: One Model for All Patch Sizes](https://mlsanity.com/paper/2212.08013/) (2022)
- [Global Context Vision Transformers](https://mlsanity.com/paper/2206.09959/) (2022)
- [GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation](https://mlsanity.com/paper/2212.06795/) (2022)
- [CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline](https://mlsanity.com/paper/2304.07277/) (2023)

## Built on by (most cited first, 1 of 1)

- [Image-Based Classification of Olive Varieties Native to Turkiye Using Multiple Deep Learning Architectures: Analysis of Performance, Complexity, and Generalization](https://mlsanity.com/paper/2602.18530/) (2026)

## Abstract (arXiv)

Glaucoma is a prevalent eye disease that progresses silently without symptoms. If not detected and treated early, it can cause permanent vision loss. Computer-assisted diagnosis systems play a crucial role in timely and efficient identification. This study introduces MaxGlaViT, a lightweight model based on the restructured Multi-Axis Vision Transformer (MaxViT) for early glaucoma detection. First, MaxViT was scaled to optimize block and channel numbers, resulting in a lighter architecture. Second, the stem was enhanced by adding attention mechanisms (CBAM, ECA, SE) after convolution layers to improve feature learning. Third, MBConv structures in MaxViT blocks were replaced by advanced DL blocks (ConvNeXt, ConvNeXtV2, InceptionNeXt). The model was evaluated using the HDV1 dataset, containing fundus images of different glaucoma stages. Additionally, 40 CNN and 40 ViT models were tested on HDV1 to validate MaxGlaViT's efficiency. Among CNN models, EfficientB6 achieved the highest accuracy (84.91%), while among ViT models, MaxViT-Tiny performed best (86.42%). The scaled MaxViT reached 87.93% accuracy. Adding ECA to the stem block increased accuracy to 89.01%. Replacing MBConv with ConvNeXtV2 further improved it to 89.87%. Finally, integrating ECA in the stem and ConvNeXtV2 in MaxViT blocks resulted in 92.03% accuracy. Testing 80 DL models for glaucoma stage classification, this study presents a comprehensive and comparative analysis. MaxGlaViT outperforms experimental and state-of-the-art models, achieving 92.03% accuracy, 92.33% precision, 92.03% recall, 92.13% f1-score, and 87.12% Cohen's kappa score.