Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

作     者：Mahmoud Ragab Ashwag Albukhari 

作者机构：Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision MedicinesKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Biochemistry DepartmentFaculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia 

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

年 卷 期：2022年第72卷第9期

页      面：5577-5591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 work was funded by the Deanship of Scientific Research(DSR) King Abdulaziz University Jeddah under Grant No.(D-398–247–1443) 

主　　题：Colorectal cancer medical data classification noise removal data classification artificial intelligence biomedical images deep learning optimizers 

摘      要：Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large *** histopathologist generally investigates the colon biopsy at the time of colonoscopy or *** detection of colorectal cancer is helpful to maintain the concept of accumulating cancer *** medical practices,histopathological investigation of tissue specimens generally takes place in a conventional way,whereas automated tools that use Artificial Intelligence(AI)techniques can produce effective results in disease detection *** this background,the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification(AAI-CCDC)*** proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal ***,AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation,Median Filtering(MF)-based noise removal,and contrast *** addition,Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature ***,Glowworm Swarm Optimization(GSO)with Stacked Gated Recurrent Unit(SGRU)model is used for the detection and classification of colorectal *** proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.