  In the wake of the global spread of monkeypox, accurate disease recognition
has become crucial. This study introduces an improved SE-InceptionV3 model,
embedding the SENet module and incorporating L2 regularization into the
InceptionV3 framework to enhance monkeypox disease detection. Utilizing the
Kaggle monkeypox dataset, which includes images of monkeypox and similar skin
conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test
set, outperforming conventional methods and deep learning models. The SENet
modules channel attention mechanism significantly elevates feature
representation, while L2 regularization ensures robust generalization.
Extensive experiments validate the models superiority in precision, recall, and
F1 score, highlighting its effectiveness in differentiating monkeypox lesions
in diverse and complex cases. The study not only provides insights into the
application of advanced CNN architectures in medical diagnostics but also opens
avenues for further research in model optimization and hyperparameter tuning
for enhanced disease recognition. https://github.com/jzc777/SE-inceptionV3-L2
