ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification

TL;DR

ConMatFormer is a hybrid deep learning model combining ConvNeXt, attention mechanisms, and transformers, achieving superior accuracy and reliability in diabetic foot ulcer classification, with enhanced interpretability through XAI methods.

Contribution

This paper introduces ConMatFormer, a novel hybrid architecture integrating ConvNeXt, attention modules, and transformers for improved DFU classification accuracy and interpretability.

Findings

Outperformed state-of-the-art CNN and ViT models in accuracy and reliability.

Achieved 0.8961 accuracy and 0.9160 precision in a single experiment.

Achieved 0.9755 accuracy with low standard deviation in 4-fold cross-validation.

Abstract

Diabetic foot ulcer (DFU) detection is a clinically significant yet challenging task due to the scarcity and variability of publicly available datasets. To solve these problems, we propose ConMatFormer, a new hybrid deep learning architecture that combines ConvNeXt blocks, multiple attention mechanisms convolutional block attention module (CBAM) and dual attention network (DANet), and transformer modules in a way that works together. This design facilitates the extraction of better local features and understanding of the global context, which allows us to model small skin patterns across different types of DFU very accurately. To address the class imbalance, we used data augmentation methods. A ConvNeXt block was used to obtain detailed local features in the initial stages. Subsequently, we compiled the model by adding a transformer module to enhance long-range dependency. This enabled us to pinpoint the DFU classes that were underrepresented or constituted minorities. Tests on the DS1 (DFUC2021) and DS2 (diabetic foot ulcer (DFU)) datasets showed that ConMatFormer outperformed state-of-the-art (SOTA) convolutional neural network (CNN) and Vision Transformer (ViT) models in terms of accuracy, reliability, and flexibility. The proposed method achieved an accuracy of 0.8961 and a precision of 0.9160 in a single experiment, which is a significant improvement over the current standards for classifying DFUs. In addition, by 4-fold cross-validation, the proposed model achieved an accuracy of 0.9755 with a standard deviation of only 0.0031. We further applied explainable artificial intelligence (XAI) methods, such as Grad-CAM, Grad-CAM++, and LIME, to consistently monitor the transparency and trustworthiness of the decision-making process.. Our findings set a new benchmark for DFU classification and provide a hybrid attention transformer framework for medical image analysis.