A Lightweight Attention CNN Model for Monkeypox Detection with Evolutionary Optimization

Skin disease are major global public health concern, and their accurate diagnosis usually demands comprehensive clinical examination, In the wake of recent interest in the development of automated detection methods for skin diseases, several artificial intelligence based techniques have been proposed; most of these techniques, however, are based on pre-trained deep learning architectures, which are computationally expensive to train, thus limiting their real-world applicability in resource-constrained clinical environments. Addressing such limitations, this work proposes a lightweight attention-based convolutional neural network optimized using a Genetic Algorithm (GA) for multi-class skin disease classification. Accordingly, the proposed framework embeds an attention mechanism to enhance lesion-relevant regions of interest while using GA-based hyperparameter optimization to improve the robustness, generalization, and classification performance of the network. The model has been tested on the Kaggle Monkeypox 2022 Remastered dataset, comprising 847 images belonging to six classes of skin diseases. Performance was evaluated using 5-fold cross-validation to ensure robustness and reliability of the reported results. The experimental outcome shows that the proposed GA-CNN-Attn framework yields an accuracy of 98.18%, precision of 98.86%, recall of 98.41%, F1-score of 98.53%, with a macro-averaged ROC–AUC of 99.3%, which is higher than that achieved by several state-of-the-art deep learning models. These findings therefore establish the proposed technique as computationally efficient yet cost-effective clinical decision support tool suitable for deployment in resource-constrained healthcare facilities.