Using novel deep learning models for rapid and efficient assistance in monkeypox screening from skin images

Abstract

Monkeypox, a communicable disease instigated by the monkeypox virus, transmits through direct contact with infectious skin lesions or mucosal blisters, posing severe complications such as pneumonia, encephalitis, and even fatality. Traditional clinical diagnostics, heavily reliant on the discerning judgment of clinical experts, are both time-consuming and labor-intensive, with inherent infection risks, underscoring the critical need for automated, efficient auxiliary diagnostic models. In response, we have developed a deep learning classification model augmented by self-attention mechanisms and feature pyramid integration, employing attentional strategies to amalgamate image features across varying scales and assimilating a priori knowledge from the VGG model to selectively capture salient features. Aiming to enhance task performance and model generalizability, we incorporated different components into the baseline model in a series of ablation studies, revealing the contribution of each component to overall model efficacy. In comparison with state-of-the-art deep learning models, our proposed model achieved the highest accuracy and precision, marking a 6% improvement over the second-best model. The results from ablation experiments corroborate the effectiveness of individual module components in enhancing model performance. Our method for diagnosing monkeypox demonstrates improved diagnostic precision and extends the reach of medical services in resource-constrained settings.