用AI提升脑癌诊断准确率，还能解释判断依据
From Images to Insights: Transforming Brain Cancer Diagnosis with Explainable AI
- 用DenseNet169模型分析6056张脑癌MRI图像
- 准确率、召回率等指标均达0.9983
- 结合GradCAM等技术实现诊断过程可视化
脑癌诊断面临精准与及时的挑战，依赖放射科医生经验常因专业人才不足而受限。尽管有影像资源，脑癌仍难识别、耗时长且类内差异大。本研究发布孟加拉脑癌MRI数据集，包含6056张图像，分为脑肿瘤、胶质瘤、脑膜瘤三类，源自孟加拉多家医院，具有多样性与现实性。采用深度学习模型，DenseNet169表现最佳，准确率、精确率、召回率和F1分数均达0.9983。同时引入GradCAM、GradCAM++、ScoreCAM和LayerCAM等可解释AI方法，可视化模型决策过程。结果表明，该模型在提升诊断精度的同时提供透明性，有助于早期发现与改善患者预后。
原文摘要 · Abstract (English)
Brain cancer represents a major challenge in medical diagnostics, requisite precise and timely detection for effective treatment. Diagnosis initially relies on the proficiency of radiologists, which can cause difficulties and threats when the expertise is sparse. Despite the use of imaging resources, brain cancer remains often difficult, time-consuming, and vulnerable to intraclass variability. This study conveys the Bangladesh Brain Cancer MRI Dataset, containing 6,056 MRI images organized into three categories: Brain Tumor, Brain Glioma, and Brain Menin. The dataset was collected from several hospitals in Bangladesh, providing a diverse and realistic sample for research. We implemented advanced deep learning models, and DenseNet169 achieved exceptional results, with accuracy, precision, recall, and F1-Score all reaching 0.9983. In addition, Explainable AI (XAI) methods including GradCAM, GradCAM++, ScoreCAM, and LayerCAM were employed to provide visual representations of the decision-making processes of the models. In the context of brain cancer, these techniques highlight DenseNet169's potential to enhance diagnostic accuracy while simultaneously offering transparency, facilitating early diagnosis and better patient outcomes.
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