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

Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*

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

* Corresponding Authors: Jahwan Koo. Email: email; Nawab Muhammad Faseeh Qureshi. Email: email

Computer Systems Science and Engineering 2023, 46(1), 287-302.


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 pre-trained 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: MobileNet, 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.


Cite This Article

M. Shaikh, I. F. Siddiqui, Q. Arain, J. Koo, M. A. Unar et al., "Mdev model: a novel ensemble-based transfer learning approach for pneumonia classification using cxr images," Computer Systems Science and Engineering, vol. 46, no.1, pp. 287–302, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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