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 

机构地区：[1]Department of Software Engineering,Mehran University of Engineering and Technology,Jamshoro,Pakistan [2]College of Software,Sungkyunkwan University,Suwon,Korea [3]Department of Computer Systems,Mehran University of Engineering and Technology,Jamshoro,Pakistan [4]Department of Computer Education,Sungkyunkwan University,Seoul,Korea

出　　处：《Computer Systems Science & Engineering》2023年第7期287-302,共16页计算机系统科学与工程（英文）

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).

摘　　要：Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The 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 images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The 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.

关 键 词：Deep transfer learning convolution neural network image processing computer vision ensemble learning pneumonia classification MDEV model 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] R563.1[医药卫生—呼吸系统]