Innovations in Brain MRI Tumor Segmentation and Classification: Machine Learning and Deep Learning Perspectives

The most demanding task in medical imaging is the identification and categorization of tumors in magnetic resonance images (MRI) of brain. This article provides a broad analysis of machine learning (ML) and deep learning (DL) models that are engaged in the identification and categorization of tumors. The study covers the traditional ML methods like support vector machines (SVM) and K-nearest neighbors (KNN), as well as DL models like convolutional neural networks (CNN) and fully convolutional networks. Further hybrid models are also reviewed to show the performance of these combinations. Moreover, challenges like imbalance in datasets, variability in tumors, and noise artifacts are examined with proper performance metrics with bench-mark datasets. Further future directions are explored by synthesizing the present knowledge to overcome the key challenges. It also offers prospective research advancements in the implementation of ML and DL approaches in brain tumor imaging. Last but not least, this article provides scope, is objective, and is clear and concise on medical imaging.