Metric-Based Meta-Learning Approach for Few-Shot Classification of Brain Tumors Using Magnetic Resonance Images

Abstract

Brain tumor prediction from magnetic resonance images is an important problem, but it is difficult due to the complexity of brain structure and variability in tumor appearance. There have been various ML and DL-based approaches, but the limitations of current models are a lack of adaptability to new tasks and a need for extensive training on large datasets. To address these issues, a novel meta-learning approach has been proposed, enabling rapid adaptation with limited data. This paper presents a method that integrates a vision transformer with a metric-based model, and few-shot learning to enhance classification performance. The proposed method begins with preprocessing MRI images, followed by feature extraction using a vision transformer. A metric-based Siamese network enhances the model's learning, enabling quick adaptation to unseen data and improving robustness. Furthermore, applying a few-shot learning strategy enhances performance when there is limited training data. A comparison of the model's performance with other developed models reveals that it consistently performs better. It has also been compared with previously proposed approaches with the same datasets using evaluation metrics including accuracy, precision, specificity, recall, and F1-score. The results demonstrate the efficacy of our methodology for brain tumor classification, which has significant implications for enhancing diagnostic accuracy and patient outcomes.