Multi-Model Deep Learning Approach for Monkeypox Detection: Evaluating Base and Enhanced CNN Architectures

Abstract

Global reemergence of monkeypox has underlined the urgency of rapid, specific and scalable diagnostic solutions. PurposeThis study investigates the possible use of deep learning techniques for automated classification of monkeypox from skin lesion images in a cohort of suspected and confirmed monkeypox patients. The study evaluates five known architecture of Convolutional Neural Networks (CNN)- VGG16, InceptionV3, MobileNet, Xception, and ResNet50 on a dataset of 998 images labelled with monkeypox, chickenpox, measles and normal. Implemented each model in a base and hybrid form, hybrid version improved by attention mechanisms and noise regularization, improves focus on lesions and encourages generalization. Evaluation Metrics for Performance Accuracy, Precision, Recall and F1- score Among all the tested models, the hybrid MobileNet and InceptionV3 models had the highest accuracies of 97% and 96%, respectively, and stable performance in classification. Hybrid versions of other models like Xception and VGG16 also performed very well. On the other hand, the ResNet50 hybrid model performed poorly, suggesting the difficulties adapting that architecture for this problem. The findings from this study corroborates that hybrid deep learning models are immensely beneficial for improving the accuracy and robustness of monkeypox classification from images. Thus, these results highlight the promise of AI-mediated diagnostic tools to address early detection and outbreak management, especially in resource- scarce settings. Research coming down the pipeline will identity suitable prospects for data expansion, transformer architectures, and integrating multimodal clinical data to ultimately yield a more reliable and clinically- applicable diagnostic.