Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

作     者：Abdullah A.Asiri Muhammad Aamir Ahmad Shaf Tariq Ali Muhammad Zeeshan Muhammad Irfan Khalaf A.Alshamrani Hassan A.Alshamrani Fawaz F.Alqahtani Ali H.D.Alshehri 

作者机构：Radiological Sciences DepartmentCollege of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Computer ScienceCOMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Computer ScienceBahauddin Zakariya UniversityMultan 66000Pakistan Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi 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12期

页      面：5735-5753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：CNN brain tumor block-wise structure VGG19 VGG16 

摘      要：The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperforms the best results associated with the existing methods.