SHAP集成的卷积诊断网络用于特征选择性医学分析
SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis
AI总结：
本文提出SHAP集成的卷积诊断网络SICDN，通过可解释特征选择和历史加权移动平均技术，在有限医学数据集上实现超过97%的分类准确率，超越四种流行CNN模型。
摘要：
本研究引入了SHAP集成的卷积诊断网络（SICDN），这是一种可解释的特征选择方法，专为有限数据集设计，以应对数据隐私法规限制医学数据集访问所带来的挑战。SICDN模型在肺炎和乳腺癌数据集的分类任务上进行了测试，展示了超过97%的准确率，并超越了四种流行的CNN模型。我们还整合了历史加权移动平均技术以增强特征选择。SICDN在医学图像预测方面显示出潜力，代码可在https://github.com/AIPMLab/SICDN获取。
英文摘要：
This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited datasets, to address the challenge posed by data privacy regulations that restrict access to medical datasets. The SICDN model was tested on classification tasks using pneumonia and breast cancer datasets, demonstrating over 97% accuracy and surpassing four popular CNN models. We also integrated a historical weighted moving average technique to enhance feature selection. The SICDN shows potential in medical image prediction, with the code available on https://github.com/AIPMLab/SICDN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
