Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error-prone and time-consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub-image dualistic histogram equalization (DSIHE) for enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation for feature extraction, followed by a fully connected classification layer. Transparency and interpretability are emphasized through the application of the Local Interpretable Model-Agnostic Explanations (LIME) method to explain model predictions. Results demonstrate DeepEBTDNet's efficacy in brain tumor detection, even across datasets, achieving a validation accuracy of 98.96% and testing accuracy of 94.0%. This study underscores the importance of explainable AI in healthcare, facilitating precise diagnoses and transparent decision-making for early brain tumor identification and improved patient outcomes.

中文翻译：

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增强脑肿瘤检测的可解释性：在 MRI 图像上使用 LIME 的新型 DeepEBTDNet 模型

早期检测脑肿瘤对于提高患者生存率至关重要，但对大量 3D MRI 图像进行手动分析可能容易出错且耗时。本研究介绍了深度可解释脑肿瘤深度网络 (DeepEBTDNet)，这是一种新颖的深度学习模型，用于将脑 MRI 分为肿瘤或正常二元分类。DeepEBTDNet 采用子图像二元直方图均衡 (DSIHE) 来增强图像质量，利用 12 个卷积层和泄漏 ReLU (LReLU) 激活进行特征提取，然后是全连接分类层。通过应用局部可解释模型不可知解释（LIME）方法来解释模型预测，强调了透明度和可解释性。结果证明 DeepEBTDNet 在脑肿瘤检测方面的功效，甚至跨数据集，验证准确率达到 98.96%，测试准确率达到 94.0%。这项研究强调了可解释的人工智能在医疗保健中的重要性，有助于精确诊断和透明决策，以实现早期脑肿瘤识别和改善患者治疗结果。