迁移学习与可解释AI用于脑肿瘤分类：一项使用来自孟加拉国MRI数据的研究
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
AI总结：
针对孟加拉国医疗资源受限下的MRI脑肿瘤诊断难题，该研究利用VGG16、VGG19、ResNet50迁移学习模型分类胶质瘤、脑膜瘤等，并结合Grad-CAM与Grad-CAM++提升可解释性，其中VGG16准确率达99.17%。
摘要：
脑肿瘤无论良性还是恶性都会带来相当大的健康风险，其中恶性肿瘤因其迅速且不受控制的增殖而更加危险，并导致恶性病变。及时识别对于改善患者预后至关重要，尤其是在孟加拉国等医疗基础设施受限的国家。人工MRI分析十分费力且容易出现误差，因此难以实现快速、高效的诊断。本研究旨在通过构建一个自动化脑肿瘤分类系统来解决这些问题，该系统使用从孟加拉国多家医院获取的MRI数据。研究采用包括VGG16、VGG19和ResNet50在内的先进深度学习模型，对胶质瘤、脑膜瘤及其他多种脑癌进行分类。研究还使用Grad-CAM和Grad-CAM++等可解释人工智能（XAI）方法，通过突出MRI扫描中影响分类的关键区域来提升模型可解释性。VGG16取得了最高准确率，达到99.17%。XAI的整合增强了系统的透明度和稳定性，使其更适合在孟加拉国这类资源有限的环境中进行临床应用。本研究强调，深度学习模型与可解释人工智能（XAI）相结合，有能力在先进医疗技术获取受限的地区改善脑肿瘤的检测和识别。
英文摘要：
Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to tackle these problems by creating an automated brain tumor classification system utilizing MRI data obtained from many hospitals in Bangladesh. Advanced deep learning models, including VGG16, VGG19, and ResNet50, were utilized to classify glioma, meningioma, and various brain cancers. Explainable AI (XAI) methodologies, such as Grad-CAM and Grad-CAM++, were employed to improve model interpretability by emphasizing the critical areas in MRI scans that influenced the categorization. VGG16 achieved the most accuracy, attaining 99.17%. The integration of XAI enhanced the system's transparency and stability, rendering it more appropriate for clinical application in resource-limited environments such as Bangladesh. This study highlights the capability of deep learning models, in conjunction with explainable artificial intelligence (XAI), to enhance brain tumor detection and identification in areas with restricted access to advanced medical technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
