Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms

While most contemporary algorithms typically utilize MRI data from a single plane, this study highlights the importance of incorporating multiplanar MRI features for enhanced performance. Specifically, tree-based machine learning algorithms were employed to compare the accuracy of individual plane analysis versus a multiplanar approach using the popular ADNI dataset. The results unequivocally demonstrate that the multiplanar approach consistently outperforms any single plane analysis in terms of classification accuracy for any given algorithm. These findings provide evidence-based results supporting the integration of multiplanar MRI features to achieve improved performance in MRI-based classification tasks. A significant improvement in accuracy of 8-10% is achieved by the utilization of multiplanar MRI features as against the single plane.