Improved MRI-Based Brain Tumor Recognition through Modified Few-Shot Learning

Abstract

This paper introduces a groundbreaking approach to detecting brain tumors in Magnetic Resonance (MR) imaging, utilizing the cutting-edge technique of Few-Shot Learning (FSL). The primary focus of our research is the study and comparison of various MR image types, with an emphasis on leveraging FSL for effective feature extraction and analysis to accurately identify brain tumors. Few-Shot Learning, a subset of machine learning, is particularly adept at learning from a limited dataset, making it highly suitable for medical imaging scenarios where large annotated datasets are often scarce. We have adapted gradient descent algorithms, traditionally used in broader machine learning contexts, to the specific challenges of MR imaging. This adaptation enables efficient and precise tumor identification and localization within the complex structure of the skull. The strength of our methodology lies in its ability to learn effectively from a small number of examples, reducing the need for extensive annotated data, which is a common bottleneck in medical imaging. Our approach is further enhanced by incorporating advanced techniques from the Few-Shot Learning domain. These techniques allow our model to generalize from limited data, providing a robust and adaptable solution for brain tumor detection. This adaptability is critical in handling the diverse range of tumor appearances and locations within MR images. Through comprehensive experiments, we demonstrate the robustness and accuracy of our Few-Shot Learning-based approach. We present a thorough comparison with existing methods, using various evaluation metrics to assess performance. Our results show a marked improvement in both accuracy and efficiency over traditional methods in tumor detection. This improvement is particularly noteworthy given the challenging nature of working with limited data. This research marks a significant step forward in medical imaging, showcasing the potential of Few-Shot Learning in achieving early and accurate diagnosis of brain tumors. Our findings open up new avenues for applying advanced machine learning techniques in medical diagnostics, where data availability is often limited.