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

翻译：在全球猴痘疫情蔓延的背景下，精准的疾病识别变得至关重要。本研究引入一种改进的SE-InceptionV3模型，通过在InceptionV3框架中嵌入SENet模块并加入L2正则化，以增强猴痘疾病的检测性能。利用Kaggle猴痘数据集（包含猴痘及类似皮肤病变的图像），我们的模型在测试集上达到了96.71%的准确率，优于传统方法和深度学习模型。SENet模块的通道注意力机制显著提升了特征表示能力，而L2正则化则确保了模型的稳健泛化。大量实验验证了该模型在精确率、召回率和F1分数上的优越性，突显了其在区分复杂多变病例中猴痘病变的有效性。本研究不仅为先进卷积神经网络架构在医学诊断中的应用提供了见解，也为通过模型优化和超参数调优以增强疾病识别能力的进一步研究开辟了途径。https://github.com/jzc777/SE-inceptionV3-L2