arXiv 2506.091612026-03-23eess.IVcs.CV

从解释到架构：基于可解释性的CNN细化用于MRI中脑肿瘤分类

From Explanations to Architecture: Explainability-Driven CNN Refinement for Brain Tumor Classification in MRI

American International Brac University Faculty of Psychology University-Bangladesh（美国国际布拉大学心理学学院（孟加拉国））；Shinawatra University Department of Computer Science（辛纳瓦特大学计算机科学系）；American International North South University（美国国际北南大学）；American International University–Bangladesh Department of Computer Science（美国国际大学–孟加拉国计算机科学系）；Multimedia University Department of Computer Science（多媒体大学计算机科学系）

Rajan Das Gupta, Md Imrul Hasan Showmick, Lei Wei, Mushfiqur Rahman Abir, Shanjida Akter, Md. Yeasin Rahat, Md. Jakir Hossen

备注：: This is the preprint version of the manuscript. It is currently being prepared for submission to an academic conference

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AI总结：

本文提出一种可解释性驱动的CNN框架，通过Grad-CAM量化层间相关性以去除低贡献层，提升模型透明度并保持分类精度，实验证明在多类脑MRI数据集上达到高准确率，支持更可信的脑肿瘤分类。

摘要：

近期脑肿瘤分类方法常报告高准确率，但依赖深度、超参数化架构，可解释性有限，难以判断预测是否由肿瘤相关证据或背景伪影等驱动。本文提出一种可解释性卷积神经网络（CNN）框架，增强模型透明度而不牺牲分类准确率。该方法支持更可信的AI在医疗中的应用，并通过启用更可靠的MRI脑肿瘤诊断和早期检测，促进SDG 3: 良好健康与福祉。不同于仅用于事后可视化，我们利用Grad-CAM量化层间相关性，指导去除低贡献层，减少不必要的深度和参数，同时促使关注判别性肿瘤区域。我们进一步通过互补的可解释性方法验证模型决策依据，结合Grad-CAM进行空间定位与SHAP和LIME进行基于属性的验证。在多类脑MRI数据集上的实验表明，所提模型在主数据集上达到98.21%的准确率，在未见数据集上达到95.74%的准确率，表明具有强跨数据集泛化能力。总体而言，所提方法在简洁性、透明度和准确性之间取得平衡，支持更可信且临床适用的脑肿瘤分类，以改善健康结果和非侵入性疾病检测。

英文摘要：

Recent brain tumor classification methods often report high accuracy but rely on deep, over-parameterized architectures with limited interpretability, making it difficult to determine whether predictions are driven by tumor-relevant evidence or by spurious cues such as background artifacts or normal tissue. We propose an explainable convolutional neural network (CNN) framework that enhances model transparency without sacrificing classification accuracy. This approach supports more trustworthy AI in healthcare and contributes to SDG 3: Good Health and Well-being by enabling more dependable MRI-based brain tumor diagnosis and earlier detection. Rather than using explainable AI solely for post hoc visualization, we employ Grad-CAM to quantify layer-wise relevance and guide the removal of low-contribution layers, reducing unnecessary depth and parameters while encouraging attention to discriminative tumor regions. We further validate the model's decision rationale using complementary explainability methods, combining Grad-CAM for spatial localization with SHAP and LIME for attribution-based verification. Experiments on multi-class brain MRI datasets show that the proposed model achieves 98.21% accuracy on the primary dataset and 95.74% accuracy on an unseen dataset, indicating strong cross-dataset generalization. Overall, the proposed approach balances simplicity, transparency, and accuracy, supporting more trustworthy and clinically applicable brain tumor classification for improved health outcomes and non-invasive disease detection.

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