Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

TL;DR

This study employs deep transfer learning models with attention mechanisms to improve image-based classification of human monkeypox, achieving high accuracy and aiding in differential diagnosis.

Contribution

It introduces the integration of CBAM attention modules with multiple deep learning models for monkeypox classification, comparing their effectiveness.

Findings

Xception-CBAM-Dense model achieved 83.89% validation accuracy.

Attention mechanisms improved model focus on relevant features.

Deep transfer learning models outperform traditional methods in disease classification.

Abstract

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.