Review on Brain Tumor Identification and Classification

Abstract

: Recent developments in artificial intelligence (AI) and machine learning (ML) have significantly advanced the detection and classification of brain tumors. Most of these breakthroughs can be traced back to new algorithms, such as deep neural networks, transfer learning, hybrid classifiers, Bayesian models, and edge detection methods. These algorithms have virtually transformed our understanding of cancer, primarily through the analysis of MRI and CT images, offering precision, stability, and speed. The goal of this study is to review the new algorithms used for detecting and/or classifying brain tumors with the help of some example methods and papers. Deep neural networks and transfer learning enable straightforward modifications to the input domain, addressing the complex issues they solve in region-based pattern recognition (e.g., MRI and CT). Hybrid classifiers improve a system's performance by merging two or more algorithms that can individually deliver optimal results. The ability to capture uncertainty is one area in which Bayesian modelling excels. Consequently, Bayesian models will have wider clinical use than edge detection techniques, which can accurately identify tumor margins but are hindered by issues such as an inadequate number of annotated datasets, difficulty in developing computational algorithms, and reduced generalization beyond the condition in which the model was trained. This article discusses the limitations of the current models in terms of their clinical applicability. This article covers changes over time in this field as well as the various means of combining those changes to harness the full power of artificial intelligence. AI can transform clinical decision-making in numerous ways, the study points out, including the imperative to implement decisions in real-time, improve the interpretability of models, and create hybrid models that merge the characteristics of different model types and their applications. Advances in imaging-related research, besides being great for medical practice, signal a significant facet of the study of the future, especially in the areas of detection and classification of brain tumors.