Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning

Romaissa Kebache; Abdelkader Laouid; Sana Sahar Guia; Mostefa Kara; Nassima Bouadem

doi:10.1145/3644713.3644759

Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning

Romaissa Kebache
, Abdelkader Laouid
, Sana Sahar Guia
, Mostefa Kara
, Nassima Bouadem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Tuberculosis (TB) is a deadly and widespread lung disease that is often not easily detectable in the early stages. Thanks to the availability of high-resolution chest X-rays, deep learning (DL) is now able to help with the successful detection of this malignant disease, along with other possible applications in the health sector. In this manuscript, a new deep-learning model for TB detection is proposed using chest X-ray image classification. To achieve this, a mixture of two popular pre-trained deep learning CNNs has been employed (VGG16 and VGG19) utilizing the ImageNet dataset, in addition to the block attention module to obtain spatial data. This method has been proven to be valid through experiments on four popular Datasets; NLM dataset, Belarus dataset, NIAID TB dataset, and RSNA-CXR dataset. The evaluation showed results in achieving an excellent accuracy of 0.9966 and 0.9978 for both training and validation sets respectively.

Original language	English
Title of host publication	ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems
Publisher	Association for Computing Machinery
Pages	352-356
Number of pages	5
ISBN (Electronic)	9798400709036
DOIs	https://doi.org/10.1145/3644713.3644759
State	Published - 21 Dec 2023
Externally published	Yes
Event	7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023 - Dubai, United Arab Emirates Duration: 21 Dec 2023 → 22 Dec 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	21/12/23 → 22/12/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence
Chest X-Ray
Deep Learning
Image Classification
ImageNet dataset
Tuberculosis Detection

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3644713.3644759

Cite this

Kebache, R., Laouid, A., Guia, S. S., Kara, M., & Bouadem, N. (2023). Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning. In ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems (pp. 352-356). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3644713.3644759

@inproceedings{b899b3d159e3431c8c0bc450851393e9,

title = "Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning",

abstract = "Tuberculosis (TB) is a deadly and widespread lung disease that is often not easily detectable in the early stages. Thanks to the availability of high-resolution chest X-rays, deep learning (DL) is now able to help with the successful detection of this malignant disease, along with other possible applications in the health sector. In this manuscript, a new deep-learning model for TB detection is proposed using chest X-ray image classification. To achieve this, a mixture of two popular pre-trained deep learning CNNs has been employed (VGG16 and VGG19) utilizing the ImageNet dataset, in addition to the block attention module to obtain spatial data. This method has been proven to be valid through experiments on four popular Datasets; NLM dataset, Belarus dataset, NIAID TB dataset, and RSNA-CXR dataset. The evaluation showed results in achieving an excellent accuracy of 0.9966 and 0.9978 for both training and validation sets respectively.",

keywords = "Artificial Intelligence, Chest X-Ray, Deep Learning, Image Classification, ImageNet dataset, Tuberculosis Detection",

author = "Romaissa Kebache and Abdelkader Laouid and Guia, \{Sana Sahar\} and Mostefa Kara and Nassima Bouadem",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023 ; Conference date: 21-12-2023 Through 22-12-2023",

year = "2023",

month = dec,

day = "21",

doi = "10.1145/3644713.3644759",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "352--356",

booktitle = "ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems",

}

Kebache, R, Laouid, A, Guia, SS, Kara, M & Bouadem, N 2023, Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning. in ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 352-356, 7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023, Dubai, United Arab Emirates, 21/12/23. https://doi.org/10.1145/3644713.3644759

Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning. / Kebache, Romaissa; Laouid, Abdelkader; Guia, Sana Sahar et al.
ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems. Association for Computing Machinery, 2023. p. 352-356 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning

AU - Kebache, Romaissa

AU - Laouid, Abdelkader

AU - Guia, Sana Sahar

AU - Kara, Mostefa

AU - Bouadem, Nassima

PY - 2023/12/21

Y1 - 2023/12/21

N2 - Tuberculosis (TB) is a deadly and widespread lung disease that is often not easily detectable in the early stages. Thanks to the availability of high-resolution chest X-rays, deep learning (DL) is now able to help with the successful detection of this malignant disease, along with other possible applications in the health sector. In this manuscript, a new deep-learning model for TB detection is proposed using chest X-ray image classification. To achieve this, a mixture of two popular pre-trained deep learning CNNs has been employed (VGG16 and VGG19) utilizing the ImageNet dataset, in addition to the block attention module to obtain spatial data. This method has been proven to be valid through experiments on four popular Datasets; NLM dataset, Belarus dataset, NIAID TB dataset, and RSNA-CXR dataset. The evaluation showed results in achieving an excellent accuracy of 0.9966 and 0.9978 for both training and validation sets respectively.

AB - Tuberculosis (TB) is a deadly and widespread lung disease that is often not easily detectable in the early stages. Thanks to the availability of high-resolution chest X-rays, deep learning (DL) is now able to help with the successful detection of this malignant disease, along with other possible applications in the health sector. In this manuscript, a new deep-learning model for TB detection is proposed using chest X-ray image classification. To achieve this, a mixture of two popular pre-trained deep learning CNNs has been employed (VGG16 and VGG19) utilizing the ImageNet dataset, in addition to the block attention module to obtain spatial data. This method has been proven to be valid through experiments on four popular Datasets; NLM dataset, Belarus dataset, NIAID TB dataset, and RSNA-CXR dataset. The evaluation showed results in achieving an excellent accuracy of 0.9966 and 0.9978 for both training and validation sets respectively.

KW - Artificial Intelligence

KW - Chest X-Ray

KW - Deep Learning

KW - Image Classification

KW - ImageNet dataset

KW - Tuberculosis Detection

UR - https://www.scopus.com/pages/publications/85193526704

U2 - 10.1145/3644713.3644759

DO - 10.1145/3644713.3644759

M3 - Conference contribution

AN - SCOPUS:85193526704

T3 - ACM International Conference Proceeding Series

SP - 352

EP - 356

BT - ICFNDS 2023 - 2023 The 7th International Conference on Future Networks and Distributed Systems

PB - Association for Computing Machinery

T2 - 7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

Y2 - 21 December 2023 through 22 December 2023

ER -

Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this