Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021;10(1):55-68.
doi: 10.1007/s13735-021-00204-7. Epub 2021 Feb 20.

Design ensemble deep learning model for pneumonia disease classification

Khalid El Asnaoui  1
Affiliations

Design ensemble deep learning model for pneumonia disease classification

Khalid El Asnaoui. Int J Multimed Inf Retr. 2021.

Abstract

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

Keywords: Computer-aided diagnosis; Covid-19; Deep learning; Ensemble deep learning; Pneumonia disease; Pneumonia multiclass classification; X-ray images.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interestWe declare that we have no conflicts of interest to disclose. Author has no received research grants from any company.

Figures

Fig. 1
Fig. 1
Flow diagram of proposed methodology
Fig. 2
Fig. 2
Accuracy and loss curve and confusion matrix of InceptionResNet_V2
Fig. 3
Fig. 3
Accuracy and loss curve and confusion matrix of MobileNet_V2
Fig. 4
Fig. 4
Accuracy and loss curve and confusion matrix of ResNet50
Fig. 5
Fig. 5
Performance measure of single and ensemble models
See this image and copyright information in PMC

References

    1. Orbann C, Sattenspiel L, Miller E, Dimka J. Defining epidemics in computer simulation models: how do definitions influence conclusions? Epidemics. 2017;19:24–32. doi: 10.1016/j.epidem.2016.12.001. - DOI - PubMed
    1. Elasnaoui K, Chawki Y, Radeva P, Idri A (2020) Automated methods for detection and classification pneumonia based on X-ray images using deep learning. arXiv preprint arXiv:2003.14363
    1. Elasnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn. 2020 doi: 10.1080/07391102.2020.1767212. - DOI - PMC - PubMed
    1. Zerouaoui H, Idri A, El Asnaoui K (2020) Machine learning and image processing for breast cancer: a systematic map. In: World conference on information systems and technologies. Springer, Cham, pp 44–53
    1. Ouhda M, El Asnaoui K, Ouanan M, Aksasse B (2017) Content-based image retrieval using convolutional neural networks. In: First international conference on real time intelligent systems. Springer, Cham, pp 463–476

LinkOut - more resources