A Cascaded Dilated Convolution Approach for Mpox Lesion Classification

TL;DR

This paper presents a novel deep learning framework called CAGA that improves Mpox lesion classification accuracy and efficiency by combining cascaded dilated convolutions and attention mechanisms, achieving state-of-the-art results.

Contribution

Introduction of the Cascaded Atrous Group Attention (CAGA) framework that enhances multi-scale feature representation and reduces redundancy in attention for skin lesion classification.

Findings

Achieved 98% accuracy on the MCSI dataset.

Reduced model parameters by 37.5% compared to baseline.

Outperformed existing methods on multiple benchmark datasets.

Abstract

The global outbreak of the Mpox virus, classified as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization, presents significant diagnostic challenges due to its visual similarity to other skin lesion diseases. Traditional diagnostic methods for Mpox, which rely on clinical symptoms and laboratory tests, are slow and labor intensive. Deep learning-based approaches for skin lesion classification offer a promising alternative. However, developing a model that balances efficiency with accuracy is crucial to ensure reliable and timely diagnosis without compromising performance. This study introduces the Cascaded Atrous Group Attention (CAGA) framework to address these challenges, combining the Cascaded Atrous Attention module and the Cascaded Group Attention mechanism. The Cascaded Atrous Attention module utilizes dilated convolutions and cascades the outputs to enhance multi-scale representation. This is integrated into the Cascaded Group Attention mechanism, which reduces redundancy in Multi-Head Self-Attention. By integrating the Cascaded Atrous Group Attention module with EfficientViT-L1 as the backbone architecture, this approach achieves state-of-the-art performance, reaching an accuracy of 98% on the Mpox Close Skin Image (MCSI) dataset while reducing model parameters by 37.5% compared to the original EfficientViT-L1. The model's robustness is demonstrated through extensive validation on two additional benchmark datasets, where it consistently outperforms existing approaches.