标题： 基于深度学习和可解释人工智能的融合脑肿瘤分类以及基于规则的推理 显示英文标题

标题： Fusion-Based Brain Tumor Classification Using Deep Learning and Explainable AI, and Rule-Based Reasoning

摘要： 从磁共振成像（MRI）中准确且可解释地分类脑肿瘤对于有效的诊断和治疗计划至关重要。 本研究提出了一种基于集成的深度学习框架，该框架使用软投票策略将MobileNetV2和DenseNet121卷积神经网络（CNNs）相结合，以对三种常见的脑肿瘤类型：胶质瘤、脑膜瘤和垂体腺瘤进行分类。 这些模型在Figshare数据集上进行了训练和评估，采用分层的5折交叉验证协议。 为了提高透明度和临床信任度，该框架集成了一个可解释的人工智能（XAI）模块，采用Grad-CAM++进行特定类别的显著性可视化，同时结合了一个符号化的临床决策规则叠加（CDRO），将预测结果映射到已建立的放射学经验法则。 与单独的CNN相比，集成分类器表现出更优越的性能，准确率为91.7%，精确率为91.9%，召回率为91.7%，F1得分为91.6%。 Grad-CAM++可视化显示了模型关注区域与专家标注的肿瘤区域之间强烈的空间一致性，Dice系数高达0.88，IoU分数高达0.78。 临床规则激活进一步验证了具有明显形态特征病例中的模型预测。 涉及五位获得认证的放射科医生的人为中心的可解释性评估显示，解释有用性（平均值=4.4）和热图区域对应性（平均值=4.0）均获得了较高的Likert量表评分，加强了该框架的临床相关性。 总体而言，所提出的方法为自动脑肿瘤分类提供了一个强大、可解释且通用的解决方案，推动了深度学习在临床神经诊断中的整合。 显示英文摘要

摘要： Accurate and interpretable classification of brain tumors from magnetic resonance imaging (MRI) is critical for effective diagnosis and treatment planning. This study presents an ensemble-based deep learning framework that combines MobileNetV2 and DenseNet121 convolutional neural networks (CNNs) using a soft voting strategy to classify three common brain tumor types: glioma, meningioma, and pituitary adenoma. The models were trained and evaluated on the Figshare dataset using a stratified 5-fold cross-validation protocol. To enhance transparency and clinical trust, the framework integrates an Explainable AI (XAI) module employing Grad-CAM++ for class-specific saliency visualization, alongside a symbolic Clinical Decision Rule Overlay (CDRO) that maps predictions to established radiological heuristics. The ensemble classifier achieved superior performance compared to individual CNNs, with an accuracy of 91.7%, precision of 91.9%, recall of 91.7%, and F1-score of 91.6%. Grad-CAM++ visualizations revealed strong spatial alignment between model attention and expert-annotated tumor regions, supported by Dice coefficients up to 0.88 and IoU scores up to 0.78. Clinical rule activation further validated model predictions in cases with distinct morphological features. A human-centered interpretability assessment involving five board-certified radiologists yielded high Likert-scale scores for both explanation usefulness (mean = 4.4) and heatmap-region correspondence (mean = 4.0), reinforcing the framework's clinical relevance. Overall, the proposed approach offers a robust, interpretable, and generalizable solution for automated brain tumor classification, advancing the integration of deep learning into clinical neurodiagnostics.