Automated Artificial Intelligence Empowered White Blood Cells Classification Model

作     者：Mohammad Yamin Abdullah M.Basahel Mona Abusurrah Sulafah M Basahel Sachi Nandan Mohanty E.Laxmi Lydia 

作者机构：Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Management Information SystemsCollege of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia E-commerce DepartmentCollege of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia School of Computer Science&Engineering(SCOPE)VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and EngineeringVignan’s Institute of Information Technology(Autonomous)VisakhapatnamAndhra Pradesh530049India 

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

年 卷 期：2023年第75卷第4期

页      面：409-425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：The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU) Jeddah Saudi Arabia has funded this project under Grant No.KEP-1–120–42 

主　　题：White blood cells cell engineering computational intelligence image classification transfer learning 

摘      要：White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.