Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh

TL;DR

This study develops an automated brain tumor classification system using deep learning and explainable AI on MRI data from Bangladesh, achieving high accuracy and improved interpretability for resource-limited healthcare settings.

Contribution

It introduces a novel application of deep learning combined with XAI for brain tumor classification using MRI data from Bangladesh, enhancing transparency and clinical relevance.

Findings

VGG16 achieved 99.17% accuracy.

XAI methods improved model interpretability.

Deep learning effectively classifies brain tumors in resource-limited settings.

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.