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Tuberculosis Abnormality Detection in Chest X-Rays: A Deep Learning Approach

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Computer Vision and Graphics (ICCVG 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12334))

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Abstract

Tuberculosis has claimed many lives, especially in developing countries. While treatment is possible, it requires an accurate diagnosis to detect the presence of tuberculosis. Several screening techniques exist and the most reliable is the chest X-ray but the necessary radiological expertise for accurately interpreting the chest X-ray images is lacking. The task of manual examination of large chest X-ray images by radiologists is time-consuming and could result in misdiagnosis as a result of a lack of expertise. Hence, a computer-aided diagnosis could perform this task quickly, accurately and drastically improve the ability to diagnose correctly and ultimately treat the disease earlier. As a result of the complexity that surrounds the manual diagnosis of chest X-ray, we propose a model that employs the use of learning algorithm (Convolutional Neural Network) to effectively learn the features associated with tuberculosis and make corresponding accurate predictions. Our model achieved 87.8% accuracy in classifying chest X-ray into abnormal and normal classes and validated against the ground-truth. Our model expresses a promising pathway in solving the diagnosis issue in early detection of tuberculosis manifestation and, hope for the radiologists and medical healthcare facilities in the developing countries.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Durban, South Africa

    Mustapha Oloko-Oba & Serestina Viriri

Authors
  1. Mustapha Oloko-Oba
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  2. Serestina Viriri
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Correspondence to Serestina Viriri .

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Editors and Affiliations

  1. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Leszek J. Chmielewski

  2. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Ryszard Kozera

  3. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Arkadiusz Orłowski

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Oloko-Oba, M., Viriri, S. (2020). Tuberculosis Abnormality Detection in Chest X-Rays: A Deep Learning Approach. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2020. Lecture Notes in Computer Science(), vol 12334. Springer, Cham. https://doi.org/10.1007/978-3-030-59006-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-59006-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59005-5

  • Online ISBN: 978-3-030-59006-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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