# Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification

arXiv: https://arxiv.org/abs/2111.06894 · HTML: https://mlsanity.com/paper/2111.06894/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations](https://mlsanity.com/paper/2106.01548/) (2021)
- [Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques](https://mlsanity.com/paper/1908.05317/) (2019)
- [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021)
- [Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier](https://mlsanity.com/paper/2010.09593/) (2020)

## Built on by (most cited first, 6 of 6)

- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers](https://mlsanity.com/paper/2112.00065/) (2021)
- [Explainable, Multi-modal Wound Infection Classification from Images Augmented with Generated Captions](https://mlsanity.com/paper/2502.20277/) (2025)
- [Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement](https://mlsanity.com/paper/2305.01044/) (2023)
- [Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers](https://mlsanity.com/paper/2405.00858/) (2024)
- [ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2510.22743/) (2025)

## Abstract (arXiv)

This paper compares well-established Convolutional Neural Networks (CNNs) to recently introduced Vision Transformers for the task of Diabetic Foot Ulcer Classification, in the context of the DFUC 2021 Grand-Challenge, in which this work attained the first position. Comprehensive experiments demonstrate that modern CNNs are still capable of outperforming Transformers in a low-data regime, likely owing to their ability for better exploiting spatial correlations. In addition, we empirically demonstrate that the recent Sharpness-Aware Minimization (SAM) optimization algorithm considerably improves the generalization capability of both kinds of models. Our results demonstrate that for this task, the combination of CNNs and the SAM optimization process results in superior performance than any other of the considered approaches.