Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey
Document Type
Article
Publication Title
IEEE Access
Abstract
Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumours to predict their behavior, determine suitable management methods, and thus improve the performance of patients. The last few years have revolutionized medical picture grouping and brain tumour studies, thanks to machine learning tools, primarily CNNs. They demonstrate much potential for nearly extracting all features of interest from medical images and using them to classify tumours with high levels of accuracy. CNNs have complied with high accuracy rates, especially compared to the other forms of machine learning in this field. Due to the inherent ability to extract various aspects of the image along with the hierarchical detail of the picture, they are more suitable for use in operations like brain tumour classification. Similarly, the advancements in the field of natural language processing with transformer models have resulted in increased interest in the practical application of these types of models in image classification tasks. However, the application of ML models in the classification of brain tumours is still limited, therefore requiring continued research in this area. Therefore, the question about CNNs and Transformers' performance differences within the framework of the brain tumour classification becomes relatively more significant. This survey aims to provide recent contributions of related work in the domain and highlight the dynamism that has emerged due to such state-of-the-art methods.
First Page
136389
Last Page
136407
DOI
10.1109/ACCESS.2024.3460380
Publication Date
1-1-2024
Recommended Citation
Chauhan, Pratikkumar; Lunagaria, Munindra; Verma, Deepak Kumar; and Vaghela, Krunal, "Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey" (2024). Open Access archive. 10725.
https://impressions.manipal.edu/open-access-archive/10725
