A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

作     者：Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri 

作者机构：Radiological Sciences DepartmentCollege of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer ScienceCOMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and SystemsUniversity of Management and TechnologyLahore54000Pakistan Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory SciencesCollage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：3983-4002页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education Kingdom of Saudi Arabia for funding this research through a Project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University Kingdom of Saudi Arabia 

主　　题：Brain tumor support vector machine convolutional neural network BraTS classification 

摘      要：Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.