A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

作     者：Abdul Rahaman Wahab Sait Mohamad Khairi Ishak 

作者机构：Department of Documents and ArchiveCenter of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic EngineeringEngineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第2期

页      面：2057-2070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：supported through the Annual Funding track by the Deanship of Scientific Research Vice Presidency for Graduate Studies and Scientific Research King Faisal University Saudi Arabia[Project No.AN000684] 

主　　题：Brain tumor medical imaging image classification handcrafted features deep learning parameter optimization 

摘      要：In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing *** purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for Brain Tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of *** order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.