标题： 从图像到洞察：通过可解释人工智能改进脑癌诊断 显示英文标题

标题： From Images to Insights: Transforming Brain Cancer Diagnosis with Explainable AI

摘要： 脑癌是医学诊断中的重大挑战，需要精确和及时的检测以实现有效的治疗。 诊断最初依赖于放射科医生的技能，当专业知识稀缺时可能会造成困难和威胁。 尽管使用了成像资源，脑癌仍然常常难以检测、耗时且容易受到类内变异的影响。 本研究提供了孟加拉国脑癌MRI数据集，包含6,056张MRI图像，分为三个类别：脑肿瘤、脑胶质瘤和脑膜瘤。 该数据集从孟加拉国几家医院收集，为研究提供了多样且真实的样本。 我们实施了先进的深度学习模型，DenseNet169取得了卓越的结果，准确率、精确率、召回率和F1分数均达到0.9983。 此外，还采用了可解释人工智能（XAI）方法，包括GradCAM、GradCAM++、ScoreCAM和LayerCAM，以提供模型决策过程的可视化表示。 在脑癌的背景下，这些技术突显了DenseNet169在提高诊断准确性的同时提供透明度的潜力，有助于早期诊断和更好的患者结果。 显示英文摘要

摘要： 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.