Attention Based Feature Fusion Network for Monkeypox Skin Lesion Detection

TL;DR

This paper presents a lightweight deep learning model combining EfficientNetV2B3 and ResNet151V2 with attention mechanisms to accurately classify monkeypox skin lesions, aiding early diagnosis amidst similar diseases.

Contribution

Introduces a novel feature fusion model with attention modules for improved monkeypox detection from skin images, outperforming existing methods.

Findings

Achieved 96.52% mean validation accuracy

Demonstrated high precision, recall, and F1-score (~96.5%)

Validated on publicly available dataset with cross-validation

Abstract

The recent monkeypox outbreak has raised significant public health concerns due to its rapid spread across multiple countries. Monkeypox can be difficult to distinguish from chickenpox and measles in the early stages because the symptoms of all three diseases are similar. Modern deep learning algorithms can be used to identify diseases, including COVID-19, by analyzing images of the affected areas. In this study, we introduce a lightweight model that merges two pre-trained architectures, EfficientNetV2B3 and ResNet151V2, to classify human monkeypox disease. We have also incorporated the squeeze-and-excitation attention network module to focus on the important parts of the feature maps for classifying the monkeypox images. This attention module provides channels and spatial attention to highlight significant areas within feature maps. We evaluated the effectiveness of our model by extensively testing it on a publicly available Monkeypox Skin Lesions Dataset using a four-fold cross-validation approach. The evaluation metrics of our model were compared with the existing others. Our model achieves a mean validation accuracy of 96.52%, with precision, recall, and F1-score values of 96.58%, 96.52%, and 96.51%, respectively.