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. this https URL

利用改进的 SE-InceptionV3 模型，本研究通过在 InceptionV3 框架中嵌入 SENet 模块和 L2 正则化的方式提高了猴痘病的检测准确性，通过对 Kaggle 猴痘数据集的测试，模型在测试集上表现出 96.71% 的显著准确度，在区分多样和复杂病例中，超越了传统方法和深度学习模型，验证了模型在精确度、召回率和 F1 得分方面的优势，不仅提供了对高级 CNN 架构在医学诊断中的应用洞察，也为进一步研究模型优化和超参数调整以提高疾病识别打开了研究途径。