Self-Supervised Learning for Brain Tumor Detection in MRI Images

Accurate and early detection of brain tumors from Magnetic Resonance Imaging (MRI) is crucial for effective clinical diagnosis and treatment planning.Although deep learning techniques have achieved promising performance in automated brain tumor detection, their success largely depends on the availability of large volumes of labeled data, which are expensive and time-consuming to obtain in the medical domain.To address this limitation, this paper presents a self-supervised learning (SSL) framework for brain tumor detection in MRI images.The proposed approach utilizes unlabeled MRI scans to learn robust feature representations during a pretraining phase using contrastive learning, followed by supervised fine-tuning with a limited labeled dataset.Experimental results on benchmark datasets including BraTS Dataset and Kaggle Brain MRI Dataset demonstrate that the SSL-based model significantly outperforms conventional supervised and transfer learning approaches, particularly in low-label scenarios.The findings indicate that self-supervised learning is a promising solution for developing reliable and scalable medical image analysis systems with reduced annotation requirements.