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MobileNetV2-based deep learning architecture with progressive transfer learning for accurate monkeypox detection
The recent monkeypox disease outbreak and its serious implications emphasize the urgent necessity for advanced diagnostic tools capable of rapid and accurate disease detection. In response, this study introduces a novel architecture for monkeypox disease identification, leveraging the capabilities of MobileNetV2 combined with progressive transfer learning and innovative model enhancements. The research began by analyzing six pre-trained models, with MobileNetV2 emerging as the most promising due to its balance of efficiency and performance. Using a self-assembled dataset, the model initially achieved an accuracy of 93.79%. Through strategic data augmentation, this performance increased to 97.85%. Further enhancements incorporated a Residual Dilated Spatial Pyramid Integration (ResDSPI) block, boosting feature extraction capabilities and elevating accuracy to 98.83%. The integration of an Efficient Channel Attention (ECA) module propelled the accuracy to an exceptional 99.34%. Each development stage was meticulously designed to ensure systematic improvements in the model’s ability to diagnose monkeypox effectively. The proposed model was rigorously tested on two datasets: the Monkeypox Skin Images Dataset (MSID) and the Monkeypox Skin Lesion Dataset (MSLD). In the binary classification scenario of the MSLD (monkeypox vs. others), our model achieved superior performance, with an accuracy of 95.55%, precision of 95.45%, and sensitivity of 92%. For the multi-class classification task on the MSID, which includes four classes (Chickenpox, Measles, Monkeypox, and Normal), the model achieved an accuracy of 92.95%, precision of 93.09%, and sensitivity of 89.00%. The model’s superior sensitivity and specificity underscore its potential as a critical asset in the surveillance and control of monkeypox outbreaks. These significant advancements not only enhance predictive performance but also mark a major step forward in the field of monkeypox disease image analysis, setting the stage for future applications in combating infectious diseases.