Self-Supervised Learning for Brain MRI Classification using Machine Learning

Brain MRI classification is essential for the early
diagnosis and treatment of brain cancers and other brain diseases
with neurological symptoms. But sometimes training an efficient
deep learning to understand from labeled MRI data is a difficult
task because of the limited availability of these data in terms of
cost effectiveness and a time consuming process. We propose a
hybrid SSL framework for brain MRI classification integrating
MRI-specific preprocessing augmentation methods with SimCLR
BYOL in this paper. The United publicly available (open-source)
brain MRI datasets were merged into a single dataset consisting
of 13604 training and testing images in four distinct classes-
glioma, meningioma, pituitary tumor and no-tumor comprising
of 4012 images.During self-supervised pretraining, the hybrid
SimCLR-BYOL framework learns complementary visual repre-
sentations through multi-view learning and anatomy-preserving
augmentations, reducing its dependence on manual annotations.
Experimental results show that the proposed SSL approach
achieves an accuracy of 96.81%, outperforming a conventional
supervised baseline that achieves 92.70% accuracy. The model
also records a macro F1-score of 96.62%, indicating reliable
performance across all categories. These findings demonstrate
that the proposed hybrid self-supervised framework can improve
brain MRI classification and provide a practical solution for
medical imaging applications where labeled data are limited