Hybrid Approach of Classification of Monkeypox Disease: Integrating Transfer Learning with ViT and Explainable AI

Human monkeypox is a persistent global health challenge, ranking among the most common illnesses worldwide. Early and accurate diagnosis is critical to developing effective treatments. This study proposes a comprehensive approach to monkeypox diagnosis using deep learning algorithms, including Vision Transformer, MobileNetV2, EfficientNetV2, ResNet-50, and a hybrid model. The hybrid model combines ResNet-50, Mo-bileNetV2, and EfficientNetV2 to reduce error rates and improve classification accuracy. The models were trained, validated, and tested on a specially curated monkeypox dataset. EfficientNetV2 demonstrated the highest training accuracy (99.94%), validation accuracy (97.80%), and testing accuracy (97.67%). ResNet-50 achieved 99.87% training accuracy, 99.85% validation accuracy, and 97.18% testing accuracy. MobileNetV2 reached 95.47% training accuracy, with validation and testing accuracies of 79.51%and 78.18%, respectively. Designed to mitigate overfitting, the Vision Transformer achieved 100% training accuracy, 87.51%validation accuracy, and 99.41% testing accuracy. Our hybrid model yielded 99.33% training accuracy and 99.09% testing accuracy. The Vision Transformer emerged as the most promising model due to its robust performance and high accuracy, followed closely by the hybrid model. Explainable AI (XAI) techniques, such as Grad-CAM, were applied to enhance the interpretability of predictions, providing visual insights into the classification process. The results underscore the potential of Vision Transformer and hybrid deep learning models for accurate and interpretable monkeypox diagnosis.

@article{Siddick2024,
  title     = {Hybrid Approach of Classification of Monkeypox Disease: Integrating Transfer Learning with ViT and Explainable AI},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {MD Abu Bakar Siddick and Zhang Yan and Mohammad Tarek Aziz and Md Mokshedur Rahman and Tanjim Mahmud and Sha Md Farid and Valisher Sapayev Odilbek Uglu and Matchanova Barno Irkinovna and Atayev Shokir Kuranbaevich and Ulugbek Hajiev},
  doi       = {10.14569/IJACSA.2024.0151286},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151286}
}