Abstract
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.
Abstract (translated)
脑肿瘤，无论是良性的还是恶性的，都会带来重大的健康风险。恶性肿瘤由于其快速且不受控制的增殖特性更加危险，并可能导致癌症扩散。及时识别是提高患者预后效果的关键，在诸如孟加拉国等医疗基础设施有限的国家更是如此。手动分析MRI图像既艰难又容易产生误差，这使得即时诊断变得低效。这项研究旨在通过利用来自多个医院的大量MRI数据创建一个自动化的脑肿瘤分类系统来解决这些问题。使用包括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。此外，采用可解释的人工智能（XAI）方法如Grad-CAM和Grad-CAM++以强调MRI扫描中影响分类的关键区域，从而提高模型的透明度与理解性。 VGG16实现了最高的准确率，达到了99.17%。结合使用XAI技术增强了系统的透明性和稳定性，在资源有限的环境中更加适合临床应用。这项研究表明，通过将深度学习模型和可解释的人工智能（XAI）相结合，可以在缺乏先进医疗科技手段的情况下显著提高脑肿瘤检测与识别的能力。
URL
https://arxiv.org/abs/2506.07228
