<p>Comparing GASHAP with previous studies.</p>

Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in these black-box models raises concerns in sensitive domains such as healthcare, where understanding the knowledge acquired to derive outcomes can be challenging. To address this concern, several strategies within the field of explainable AI (XAI) have been developed to enhance model interpretability. This study introduces a novel XAI technique, GASHAP, which integrates a genetic algorithm (GA) with SHapley Additive exPlanations (SHAP) to improve the explainability of our 3D convolutional neural network (3D-CNN) model. The model is designed to classify magnetic resonance imaging (MRI) brain scans of individuals with Alzheimer’s disease and cognitively normal controls. Deep SHAP, a widely used XAI technique, facilitates the understanding of the influence exerted by various voxels on the final classification outcome (Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, 2017. 4765–74. https://doi.org/10.5555/3295222.3295230). However, voxel-level representation alone lacks interpretive clarity. Therefore, the objective of this study is to provide findings at the level of anatomically defined brain regions. Critical regions are identified by leveraging their SHAP values, followed by the application of a genetic algorithm to generate a definitive mask highlighting the most significant regions for Alzheimer’s disease diagnosis (Shahamat H, Saniee Abadeh M. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw. 2020;126:218–34. https://doi.org/10.1016/j.neunet.2020.03.017 PMID: 32259762). The research commenced by implementing a 3D-CNN for MRI image classification. Subsequently, the GASHAP technique was applied to enhance model transparency. The final result is a brain mask that delineates the pertinent regions crucial for Alzheimer’s disease diagnosis. Finally, a comparative analysis is conducted between our findings and those of previous studies.卷积神经网络（Convolutional Neural Networks, CNNs）因在图像分类任务中具备高精度表现而广受认可。然而，这类黑箱模型缺乏可解释性的问题，在医疗健康等敏感领域引发了诸多担忧——在这些领域中，理解模型为得出预测结果所习得的知识往往极具挑战。为解决这一问题，可解释人工智能（Explainable Artificial Intelligence, XAI）领域已提出多种策略以提升模型的可解释性。本研究提出一种全新的XAI技术GASHAP，该方法将遗传算法（Genetic Algorithm, GA）与夏普利可加解释（SHapley Additive exPlanations, SHAP）相结合，以提升我们所构建的三维卷积神经网络（3D Convolutional Neural Network, 3D-CNN）模型的可解释性。该模型旨在对阿尔茨海默病患者与认知正常对照者的磁共振成像（Magnetic Resonance Imaging, MRI）脑部扫描图像进行分类。深度SHAP作为一种广泛应用的XAI技术，可帮助研究者理解不同体素（voxel）对最终分类结果的影响（Lundberg SM, Lee SI. 用于解释模型预测的统一方法. 见：《神经信息处理系统进展》, 2017: 4765-4774. https://doi.org/10.5555/3295222.3295230）。然而，仅依靠体素级的表征仍缺乏足够的解释清晰度。因此，本研究的目标是在解剖学定义的脑区层面呈现研究结果。研究人员首先通过体素的SHAP值识别关键脑区，随后应用遗传算法生成精准的掩码，以高亮显示阿尔茨海默病诊断中最为重要的脑区（Shahamat H, Saniee Abadeh M. 基于深度学习演化方法的脑部MRI分析. 《神经网络》, 2020;126:218-234. https://doi.org/10.1016/j.neunet.2020.03.017 PMID: 32259762）。本研究首先构建了用于MRI图像分类的3D-CNN模型，随后应用GASHAP技术提升模型的可解释性。最终得到的结果为脑部掩码，可勾勒出阿尔茨海默病诊断中至关重要的相关脑区。最后，本研究将所得结果与既往研究成果进行了对比分析。