Explainable AI-based method for brain abnormality diagnostics using MRI

Abstract

Detecting brain abnormalities using magnetic resonance imaging (MRI) is a vital frontier in neurological research. Therefore, accurate methods are essential for guiding neurologists in diagnosing enigmatic disorders such as Alzheimer’s disease (AD) and brain tumors. These methods aid in the early detection and treatment of these formidable conditions. However, traditional techniques often suffer from high computational complexity and efficiency. Additionally, existing detection models lack the ability to explain their predictions, rendering them untrustworthy for clinicians. This study presents an explainable framework for automatic brain abnormality detection in MRI images. The methodology includes a robust preprocessing pipeline that ameliorates image relevance through image thresholding, morphological operations and adaptive edge detection using the AutoCanny algorithm. AutoCanny method automatically adjusts thresholds to ensure effective edge detection across different images. Then, the MRI images are fed to efficient vision transformer model (EfficientViT) for classification. EfficientViT features a memory-efficient sandwich layout, cascaded group attention module and optimized parameter reallocation. These innovations collectively enhanced the model efficiency in terms of memory usage, computational complexity and parameter optimization. Moreover, gradient-based Shapley additive explanations is employed to explain the EfficientViT model predictions. EfficientViT achieved the highest accuracy of 99.24%, 97.1%, 99.5% and 98.87% on the AD, Tumor1, Tumor2 and merged datasets, respectively. Furthermore, the proposed model outperformed longstanding deep learning techniques. These findings have significant implications for uncovering hidden information associated with brain abnormality as well as improving the diagnostic process and treatment planning. Our model can aid neurologists in the validation of manual MRI neurological disorders screenings.