MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

作     者：Mehwish Shaikh Isma Farah Siddiqui Qasim Arain Jahwan Koo Mukhtiar Ali Unar Nawab Muhammad Faseeh Qureshi 

作者机构：Department of Software EngineeringMehran University of Engineering and TechnologyJamshoroPakistan College of SoftwareSungkyunkwan UniversitySuwonKorea Department of Computer SystemsMehran University of Engineering and TechnologyJamshoroPakistan Department of Computer EducationSungkyunkwan UniversitySeoulKorea 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第7期

页      面：287-302页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

基　　金：This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A1A01052299) 

主　　题：Deep transfer learning convolution neural network image processing computer vision ensemble learning pneumonia classification MDEV model 

摘      要：Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is *** physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a *** input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’***,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest *** medical classifications,deep convolution neural networks are commonly *** research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and *** MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR *** evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and *** model effectively decreases training loss while increasing *** findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.