Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

作     者：Mahmoud Ragab Hesham A.Abdushkour Alaa F.Nahhas Wajdi H.Aljedaibi 

作者机构：Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision MedicinesKing Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Nautical Science DepartmentFaculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry DepartmentFaculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi 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10期

页      面：533-546页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 0401[教育学-教育学] 1002[医学-临床医学] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：This work was funded by the Deanship of Scientific Research(DSR) King Abdulaziz University Jeddah Under Grant No.(D-782-980-1443). 

主　　题：Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization 

摘      要：Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.