Affiliations: [a] Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, UP, India | [b] Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, UP, India
Correspondence:
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Corresponding author: Sachin Jain, Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, UP, India. E-mail: sachincs86@gmail.com.
Abstract: There has been extensive use of machine learning (ML) based tools for mathematical symbol and phrase categorization and prediction. Aiming to thoroughly analyze the existing methods for categorizing brain tumors, this paper considers both machine-learning and non-machine-learning approaches. From 2013 to 2023, the writers compiled and reviewed research papers on brain tumor detection. Wiley, IEEE-Explore, Science-Direct, Scopus, ACM-Digital Library, and others provide the relevant data. A systematic literature review examines the efficacy of research methodologies over the last ten years or more by compiling relevant publications and studies from various sources. Accuracy, sensitivity, specificity, and computing efficiency are some of the criteria that researchers use to evaluate these methods. The availability of labeled data, the required degree of automation and accuracy in the classification process, and the unique dataset are generally the deciding factors in the method choice. This work integrates previous research findings to summarize the current state of brain tumor categorization. This paper summarizes the 169 research papers in brain tumor detection between 2013–2023 and explores the application and development of machine learning methods in brain tumor detection, which has significant research implications and value in the field of brain tumor classification research. All research findings of previous studies are arranged in this paper in the form of research questions and answers format.
Keywords: Brain tumor classification, machine learning, deep learning, SVM, CNN
