Abstract
Monkeypox has arisen as a global health issue, requiring prompt and precise diagnosis for optimal management. Conventional diagnostic techniques, including PCR, are dependable yet frequently unattainable in resource-constrained environments. Deep learning demonstrates potential in automating disease detection from skin lesion images; nevertheless, current models are hindered by limits in feature extraction and misclassification challenges. This paper presents an attention-augmented deep learning architecture to enhance classification accuracy for monkeypox and other dermatological conditions. This work presents a model based on EfficientNetB7, augmented with coordinate attention to enhance feature extraction and classification accuracy. The Monkeypox Skin Lesion Dataset (MSLD v2.0) is utilised, incorporating pre-processing methods such as image normalisation, scaling, and data augmentation. Diverse edge detection techniques are examined to enhance feature representation. The model is subjected to five-fold cross-validation and is evaluated against Xception, Swin Transformer, ResNet-50, MobileNetV2, and baseline EfficientNet models, utilising accuracy, precision, recall, F1-score, and AUC as assessment measures. Our model attains an unparalleled accuracy of 99.99%, precision of 99.8%, recall of 99.9%, F1-score of 99.85%, and an AUC of 100%. In contrast to previous studies that indicated a maximum accuracy of 98.81%, our methodology markedly diminishes false negatives and improves generalisation. This research sets a novel standard for AI-based monkeypox detection, showcasing exceptional accuracy and resilience. The results endorse the incorporation of AI-driven diagnostic tools in clinical and telemedicine settings, with prospects for immediate implementation and extensive epidemiological monitoring.