Deep Learning Models for 3D MRI Brain Classification - A Multi-sequence Comparison

Marius Pullig, Benjamin Bergner, Amish Doshi, Anja Hennemuth, Zahi A. Fayad, Christoph Lippert

Abstract: This study evaluates the diagnostic performance for binary abnormality classification of deep learning models on various types of sequences from a multidisease clinical brain MRI dataset. Additionally, it determines the influence of the sample size and the type of disease. The sequences are DWI, FLAIR, T1- weighted, T1-weighted FLAIR, T2-weighted and T2-weighted FLAIR. On the full-sized multi-disease, the best performance is achieved on the T2-weighted FLAIR sequence using a VGG-16 dataset resulting in an AUC value of 0.89. The work highlights the importance of carefully selecting MRI sequences for deep learning and identifies discrepancies to screening protocols for physicians.