Optimizing deep learning with attention techniques for improved detection of human monkeypox lesions

Abstract

Early and accurate detection of human monkeypox is vital for timely intervention and outbreak control. Traditional diagnostic methods are slow, error-prone, and often struggle to distinguish monkeypox lesions from visually similar skin conditions. To address these challenges, it propose an Optimized Colony Weighted Hybrid Pooling Attentive ConvNet (OCWPC), a novel deep learning framework that integrates Ant Colony Optimization (ACO) for robust feature selection and a Weighted Hybrid Pooling Attention (WHPA) mechanism to enhance lesion-specific feature extraction. The approach leverages multiple preprocessing steps, brightness and contrast enhancement, median filtering, unsharp masking, and Otsu threshold segmentation, followed by Scale-Invariant Feature Transform (SIFT) and Gaussian augmentation to improve feature robustness and generalization. The model was trained and validated on publicly available Human Monkeypox datasets, achieving superior results with 99.49% accuracy, 99.49% precision, 99.49% recall, and 98.98% mAP. Comparative evaluation against state-of-the-art models confirms the effectiveness of OCWPC in minimizing misclassification and improving reliability. These findings highlight the model’s potential for real-world clinical deployment and automated large-scale screening to strengthen monkeypox surveillance and management.