Performance evaluation of optimized deep learning model with Multilayered Max-Norm Regularization (MMNR) technique for brain tumour classification in MRI multi-modal images

Brain tumours are aggressive malignant diseases, both in children and adults, representing 86 to 92 percent of all primary and almost half of secondary Central Nervous System (CNS) tumours. For individuals with malignant brain or central nervous system (CNS) tumours, the 5-year survival rate is about 34% for males and 36% for women. Brain tumours can be classified into several types, including benign, malignant, pituitary, etc. This study proposes a new architecture named Multilayered Max-Norm Regularization CNN(MMNR-CNN) and investigates the performance of this model for the classification of brain tumours in multi-modal MRI images. The model incorporates Markov Random Field (MRF) for bias field correction, and Monte Carlo Dropout to quantify prediction uncertainty through stochastic forward passes, enhancing the model's reliability in clinical decision-making. Furthermore, we integrate Explainable AI (XAI) techniques using Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret the regions of MRI scans that contribute most to the classification decisions. We present a complete analysis of the Multilayered Max-Norm Regularization model trained on augmented brain image data and compare the performance on different values of regularization parameters that lead to the automatic selection of spatially important features for the classification task. This increases the generalization and robustness of the training dataset through augmentation. The model is trained using the Br35H database and the Figshare database. Both are used primarily for research in brain tumour detection and classification. The obtained performance metrics are the best in the literature, with a testing accuracy of 99.88% and 100 % precision.