Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

作     者：Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil 

作者机构：Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision MedicinesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy PracticeFaculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering DepartmentFaculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science DepartmentFaculty of Computers and InformationLuxor University85951Egypt Mathematics DepartmentFaculty of ScienceSouth Valley UniversityQena83523Egypt Department of MathematicsFaculty of ScienceTaif UniversityTaif21944Saudi Arabia 

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

年 卷 期：2022年第71卷第6期

页      面：5751-5764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：This research work was funded by Institution Fund projects under Grant No.(IFPRC-214-166-2020)Therefore authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University DSR Jeddah Saudi Arabia 

主　　题：Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning 

摘      要：The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.