A systematic analysis of magnetic resonance images and deep learning methods used for diagnosis of brain tumor

• Published in: Multimedia tools and applications Vol. 83; no. 8; pp. 23929 - 23966
• Main Authors: Solanki, Shubhangi, Singh, Uday Pratap, Chouhan, Siddharth Singh, Jain, Sanjeev
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Automation; Brain; Brain cancer; Classification; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Deep learning; Diagnosis; Image segmentation; Machine learning; Magnetic resonance imaging; Medical imaging; Multimedia Information Systems; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Tumors; Brain tumor; Deep learning; Segmentation; Magnetic resonance image; Classification
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-16430-6

Accurate classification and segmentation of brain tumors is a critical task to perform. The term classification is the process of grading tumors i.e., whether the tumor is Malignant (cancerous) and Benign (not cancerous), and segmentation is the process of extracting the region of interest. In the last few years, with the development of approaches like computer vision, deep learning, and machine learning algorithms, Magnetic Resonance Images (MRI) are the most widely used modality for the purpose of tumor screening and diagnosing. This process is automated in nature and also attain higher accuracy. Nowadays, physicians also practice MRI automated diagnosis systems, so that the diagnosis is faster, reliable, automated, reproducible, and more prominently less expensive. So here in this paper, we present an extensive survey of brain tumor classification and segmentation approaches based on MRI images. This manuscript mainly explores recently used deep learning methods and approaches. Finally, the paper concludes with various state-of-the-art findings.