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Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning

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Part of the book series: Studies in Big Data ((SBD,volume 90))

Abstract

Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and Computed Tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, covid-19, etc., and aid in its containment. Medical image analysis is one of the most promising research areas; it provides facilities for diagnosis and making decisions of several diseases such as MERS, covid-19, etc. In this paper, we present a comparison of recent deep convolutional neural network (CNN) architectures for automatic binary classification of pneumonia images based on fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception) and a retraining of a baseline CNN. The proposed work has been tested using chest X-Ray & CT dataset, which contains 6087 images (4504 pneumonia and 1583 normal). As a result, we can conclude that the fine-tuned version of Resnet50 shows highly satisfactory performance with rate of increase in training and testing accuracy (more than 96% of accuracy).

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Authors and Affiliations

  1. National School of Applied Sciences (ENSAO), Department of Electronics, Computer Sciences, and Telecommunications, Laboratory Smart Information, Communication and Technologies “SmarICT Lab”, Mohammed First University, BP: 669, 60000, Oujda, Morocco

    Khalid El Asnaoui Assistant Professor

  2. Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco

    Youness Chawki

  3. Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco

    Ali Idri

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  1. Khalid El Asnaoui Assistant Professor
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  2. Youness Chawki
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  3. Ali Idri
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The experiments and the programming stage were carried out by Khalid El Asnaoui. All authors wrote the paper, and all approve this submission.

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Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Yassine Maleh

  2. Chouaib Doukkali University, El Jadida, Morocco

    Youssef Baddi

  3. Charles Darwin University, Canberra, Australia

    Mamoun Alazab

  4. Texas A&M University, San Antonio, USA

    Loai Tawalbeh

  5. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

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El Asnaoui, K., Chawki, Y., Idri, A. (2021). Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning. In: Maleh, Y., Baddi, Y., Alazab, M., Tawalbeh, L., Romdhani, I. (eds) Artificial Intelligence and Blockchain for Future Cybersecurity Applications. Studies in Big Data, vol 90. Springer, Cham. https://doi.org/10.1007/978-3-030-74575-2_14

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