Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

作     者：Mudassir Khalil Muhammad Imran Sharif Ahmed Naeem Muhammad Umar Chaudhry Hafiz Tayyab Rauf Adham E.Ragab 

作者机构：Department of Computer EngineeringBahauddin Zakariya UniversityMultan60000Pakistan Department of Computer ScienceKansas State UniversityManhattanKS66506USA Department of Computer ScienceUniversity of Management and TechnologyLahore54000Pakistan Centre for Smart SystemsAI and CybersecurityStaffordshire UniversityStoke-on-TrentST42DEUK Industrial Engineering DepartmentCollage of EngineeringKing Saud UniversityPO Box 800Riyadh11421Saudi Arabia 

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

年 卷 期：2023年第77卷第11期

页      面：2031-2047页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funding this work through Researchers Supporting Project Number(RSPD2023R711) King Saud University Riyadh Saudi Arabia 

主　　题：Brain tumor deep learning feature extraction feature selection feature fusion transfer learning 

摘      要：Early detection of brain tumors is critical for effective treatment *** tumors in their nascent stages can significantly enhance the chances of patient *** there are various types of brain tumors,each with unique characteristics and treatment protocols,tumors are often minuscule during their initial stages,making manual diagnosis challenging,time-consuming,and potentially *** techniques predominantly used in hospitals involve manual detection via MRI scans,which can be costly,error-prone,and *** automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest *** research applies several data augmentation techniques to enhance the dataset for diagnosis,including rotations of 90 and 180 degrees and inverting along vertical and horizontal *** CIELAB color space is employed for tumor image selection and ROI *** deep learning models,such as DarkNet-53 and AlexNet,are applied to extract features from the fully connected layers,following the feature selection using entropy-coded Particle Swarm Optimization(PSO).The selected features are further processed through multiple SVM kernels for *** study furthers medical imaging with its automated approach to brain tumor detection,significantly minimizing the time and cost of a manual *** method heightens the possibilities of an earlier tumor identification,creating an avenue for more successful treatment planning and better overall patient outcomes.