Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods

• Published in: Neural computing & applications Vol. 37; no. 6; pp. 4697 - 4731
• Main Authors: Reis, Hatice Catal, Turk, Veysel
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Brain; Brain cancer; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Diagnosis; Glioma; Graph theory; Heuristic methods; Image Processing and Computer Vision; Machine learning; Original Article; Probability and Statistics in Computer Science; Segmentation; Tumors; Deep learning; Computer vision; Graph theory; Image enhancement techniques; Brain tumor detection; Metaheuristic algorithms; Re-transfer learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10629-w

Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.