Abstract:

This study systematically reviewed advancements in brain tumor classification using convolutional neural networks (CNNs) with MRI data, aiming to assess the effectiveness and potential for future enhancements. An analysis of 37 studies demonstrated the extensive use of CNN architectures and pre-processing techniques, achieving high classification accuracy rates. However, challenges such as class imbalances and model interpretability were identified. To address these issues, the study recommends further exploration of advanced deep learning techniques, ensemble methods, and the inclusion of more diverse datasets. A maximum accuracy of 98.80% was reported on a dataset comprising 154 MRI brain images, demonstrating the effectiveness of CNNs in brain cancer detection. Additionally, a five-year meta-analysis (2018-2022) on MRI scan cases across different demographic groups revealed important patterns in healthcare resource allocation. This research not only provides a comprehensive evaluation of CNN usage in MRI-based brain tumor classification but also outlines directions for future research to enhance diagnostic capabilities.