Abdullah A. Asiri1, Toufique A. Soomro2,3,*, Ahmed Ali4, Faisal Bin Ubaid5, Muhammad Irfan6,*, Khlood M. Mehdar7, Magbool Alelyani8, Mohammed S. Alshuhri9, Ahmad Joman Alghamdi10, Sultan Alamri10
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 255-287, 2025, DOI:10.32604/cmes.2025.061683
- 11 April 2025
Abstract Global mortality rates are greatly impacted by malignancies of the brain and nervous system. Although, Magnetic Resonance Imaging (MRI) plays a pivotal role in detecting brain tumors; however, manual assessment is time-consuming and susceptible to human error. To address this, we introduce ICA2-SVM, an advanced computational framework integrating Independent Component Analysis Architecture-2 (ICA2) and Support Vector Machine (SVM) for automated tumor segmentation and classification. ICA2 is utilized for image preprocessing and optimization, enhancing MRI consistency and contrast. The Fast-Marching Method (FMM) is employed to delineate tumor regions, followed by SVM for precise classification. Validation on More >
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