Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
This work addresses the problem of timely and accurate brain tumor diagnosis in resource-limited healthcare settings like Bangladesh, though it is incremental as it applies existing deep learning and XAI methods to a new regional dataset.
The study tackled automated brain tumor classification from MRI data in Bangladesh using deep learning models like VGG16, VGG19, and ResNet50, achieving up to 99.17% accuracy with VGG16, and integrated explainable AI (XAI) methods such as Grad-CAM and Grad-CAM++ to improve interpretability for clinical use.
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.