Enhancing Interpretability for Automated Human Monkeypox Skin Lesions Detection Using Pre-trained Transfer Learning Networks

Abstract

Identification of skin lesions remains a challenging task because of low image contrast, large variations in disease manifestations, and strong visual similarities among dermatological conditions. Deep transfer learning (TL)-integrated artificial intelligence (AI) systems offer an effective solution for rapid and accurate computer-aided diagnosis of multiple infectious skin diseases. In this study, a TL-based framework is proposed as an automated human monkeypox (MP) skin lesion detection (HMSLD) for monkeypox (MP), chickenpox (CP), measles (MS), and normal skin lesion classification. A publicly available dataset comprising 654 RGB clinical images. The HMSLD performed image preprocessing, data augmentation, class imbalance handling through inverse class weighting, and comparative analysis of pre-trained TL models, namely Xception, ResNet152V2, NASNetLarge, EfficientNetB0, EfficientNetB7, MobileNetV2, and MobileNetV3. MobileNetV3 performed best among the models. Their overall performance with an accuracy = 91%, precision = 91%, recall = 89%, F1-score = 90%, and an AUROC of 0.99, indicating excellent discriminative capability. MobileNetV3 achieved 96% precision for measles and 100% recall for normal skin, while EfficientNetB0 performed the highest precision at 96% and an F1-score of 93%. MobileNetV3 provides the most balanced trade-off among the evaluated models between classification accuracy, robustness, and computational efficiency. These evaluated performances indicate the potential of the HMSLD framework. It may be a reliable and cost-effective computer-aided diagnostic tool for early MP screening and other clinically similar skin diseases.