使用卷积神经网络和可解释AI基于MRI扫描诊断痴呆症
Using a Convolutional Neural Network and Explainable AI to Diagnose Dementia Based on MRI Scans
AI总结：
为提高基于MRI的痴呆症诊断准确性，研究利用6400张MRI扫描训练卷积神经网络达到98%验证准确率，并开发可解释AI算法可视化卷积滤波器输出以突出感兴趣区域，构建了兼具高分类准确性与结果可解释性的医疗辅助诊断系统。
摘要：
随着痴呆症患者数量的增加，对准确诊断程序的需求也在上升。当前的方法，如使用MRI扫描，依赖人工输入，这可能不准确。然而，机器学习算法背后的决策逻辑及其输出无法被解释，因为大多数在黑盒模型中运行。因此，为了提高通过MRI诊断痴呆症的准确性，使用一个包含6400个MRI扫描的开源数据库开发并训练了一个卷积神经网络，该数据库分为4个痴呆症类别。该模型达到了98%的验证准确率，显示出良好的拟合度并能泛化到新数据。此外，为了辅助模型输出的可视化，通过可视化每个卷积层中各个滤波器的输出，开发了一种可解释AI算法，突出了扫描中的感兴趣区域。这些输出很好地识别了对模型分类贡献最大的图像特征，从而允许用户可视化和理解结果。总而言之，卷积神经网络和可解释AI算法的结合创造了一个可用于医疗领域的系统，不仅有助于正确分类痴呆症，还允许所有相关人员可视化和理解结果。
英文摘要：
As the number of dementia patients rises, the need for accurate diagnostic procedures rises as well. Current methods, like using an MRI scan, rely on human input, which can be inaccurate. However, the decision logic behind machine learning algorithms and their outputs cannot be explained, as most operate in black-box models. Therefore, to increase the accuracy of diagnosing dementia through MRIs, a convolution neural network has been developed and trained using an open-source database of 6400 MRI scans divided into 4 dementia classes. The model, which attained a 98 percent validation accuracy, was shown to be well fit and able to generalize to new data. Furthermore, to aid in the visualization of the model output, an explainable AI algorithm was developed by visualizing the outputs of individual filters in each convolution layer, which highlighted regions of interest in the scan. These outputs do a great job of identifying the image features that contribute most to the model classification, thus allowing users to visualize and understand the results. Altogether, this combination of the convolution neural network and explainable AI algorithm creates a system that can be used in the medical field to not only aid in the proper classification of dementia but also allow everyone involved to visualize and understand the results.
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。
