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@article{182266,
        author = {Sai Vimenthan V L},
        title = {Monkeypox Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1954-1956},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182266},
        abstract = {The rapid spread of monkeypox (mpox) across several nations has made the current outbreak a serious public health concern. Effective management and treatment of mpox depend on early diagnosis and detection. In light of this, the goal of this study was to identify and evaluate the top-performing model for mpox detection utilizing classification models and deep learning techniques. In order to accomplish this, we analysed the accuracy levels of five popular pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2) and assessed how well they performed in mpox detection. The models accuracy, recall, precision, and F1-score were among the criteria used to evaluate their performance. The MobileNetV2 model performed the best in classification, according to our trial data, with an accuracy level of 98.16%, recall of 0.96, precision of 0.99, and F1-score of 0.98. Furthermore, testing the model on various datasets revealed that the MobileNetV2 model had the best accuracy, at 0.94%. Our results show that the MobileNetV2 approach performs better in mpox picture classification than earlier models reported in the literature.These findings are encouraging because they demonstrate the potential of machine learning methods for mpox early diagnosis. In both the training and test sets, our system demonstrated a highdegree of accuracy in mpox classification, possibly making it a useful tool for prompt and precise diagnosis in clinical situations.},
        keywords = {MobileNetV2, deep learning model, Monkeypox, Classification},
        month = {July},
        }