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

作　　者：Abdullah A Asiri Toufique A.Soomro Ahmed Ali Faisal Bin Ubaid Muhammad Irfan Khlood M.Mehdar Magbool Alelyani Mohammed S.Alshuhri Ahmad Joman Alghamdi Sultan Alamri 

机构地区：[1]Radiological Sciences Department,College of Applied Medical Sciences,Najran University,Najran,61441,Saudi Arabia [2]Artificial Intelligence and Cyber Futures Institute,Charles University,Bathurst,NSW 2795,Australia [3]Department of Electronic Engineering,The University of Larkano,Larkana,75660,Pakistan [4]Electrical Engineering Department,Sukkur IBA University,Sukkur,65200,Pakistan [5]Computer Science Department,Sukkur IBA University,Sukkur,65200,Pakistan [6]Electrical Engineering Department,College of Engineering,Najran University,Najran,61441,Saudi Arabia [7]Anatomy Department,Medicine College,Najran University,Najran,61441,Saudi Arabia [8]Department of Radiological Sciences,College of Applied Medical Science,King Khalid University,Guraiger,Abha,62521,Saudi Arabia [9]Radiology andMedical Imaging Department,College of AppliedMedical Sciences,Prince Sattam bin Abdulaziz University,Kharj,11942,Saudi Arabia [10]Radiological Sciences Department,College of Applied Medical Sciences,Taif University,Taif,21944,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期255-287,共33页工程与科学中的计算机建模(英文)

基　　金：supported by the Deanship of Graduate Studies and Scientific Research at Najran University through funding code NU/GP/MRC/13/771-1.

摘　　要：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-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)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,themodel 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.

关 键 词：Brain image segmentation MR brain enhancement independent component analysis brain tumor 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]