# Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement

arXiv: https://arxiv.org/abs/2305.01044 · HTML: https://mlsanity.com/paper/2305.01044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 1 May 2023.

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

- [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)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks](https://mlsanity.com/paper/2102.11600/) (2021)
- [Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques](https://mlsanity.com/paper/1908.05317/) (2019)
- [Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation](https://mlsanity.com/paper/2010.03341/) (2020)
- [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/1711.10448/) (2017)
- [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (2021)
- [Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2111.06894/) (2021)
- [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)

## Abstract (arXiv)

DFU is a severe complication of diabetes that can lead to amputation of the lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer Grand Challenge, researchers designed automated multi-class classification of DFU, including infection, ischaemia, both of these conditions, and none of these conditions. However, it remains a challenge as classification accuracy is still not satisfactory. This paper proposes a Venn Diagram interpretation of multi-label CNN-based method, utilizing different image enhancement strategies, to improve the multi-class DFU classification. We propose to reduce the four classes into two since both class wounds can be interpreted as the simultaneous occurrence of infection and ischaemia and none class wounds as the absence of infection and ischaemia. We introduce a novel Venn Diagram representation block in the classifier to interpret all four classes from these two classes. To make our model more resilient, we propose enhancing the perceptual quality of DFU images, particularly blurry or inconsistently lit DFU images, by performing color and sharpness enhancements on them. We also employ a fine-tuned optimization technique, adaptive sharpness aware minimization, to improve the CNN model generalization performance. The proposed method is evaluated on the test dataset of DFUC2021, containing 5,734 images and the results are compared with the top-3 winning entries of DFUC2021. Our proposed approach outperforms these existing approaches and achieves Macro-Average F1, Recall and Precision scores of 0.6592, 0.6593, and 0.6652, respectively.Additionally, We perform ablation studies and image quality measurements to further interpret our proposed method. This proposed method will benefit patients with DFUs since it tackles the inconsistencies in captured images and can be employed for a more robust remote DFU wound classification.