Advanced Computational Modeling for Brain Tumor Detection: Enhancing Segmentation Accuracy Using ICA-I and ICA-II Techniques

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 the Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) dataset demonstrates the superior performance of ICA2-SVM, achieving a Dice Similarity Coefficient (DSC) of 0.974, accuracy of 0.992, specificity of 0.99, and sensitivity of 0.99. Additionally, the model surpasses existing approaches in computational efficiency, completing analysis within 0.41 s. By integrating state-of-the-art computational techniques, ICA2-SVM advances biomedical imaging, offering a highly accurate and efficient solution for brain tumor detection. Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.