research-article

Chest Diseases Classification Using CXR and Deep Ensemble Learning

Authors:

Adnane Ait Nasser,

Moulay A. AkhloufiAuthors Info & Claims

CBMI '22: Proceedings of the 19th International Conference on Content-based Multimedia Indexing

Pages 116 - 120

https://doi.org/10.1145/3549555.3549581

Published: 07 October 2022 Publication History

Abstract

Chest diseases are among the most common worldwide health problems; they are potentially life-threatening disorders which can affect organs such as lungs and heart. Radiologists typically use visual inspection to diagnose chest X-ray (CXR) diseases, which is a difficult task prone to errors. The signs of chest abnormalities appear as opacities around the affected organ, making it difficult to distinguish between diseases of superimposed organs. To this end, we propose a very first method for CXR organ disease detection using deep learning. We used an ensemble learning (EL) approach to increase the efficiency of the classification of CXR diseases by organs (lung and heart) using a consolidated dataset. This dataset contains 26,316 CXR images from VinDr-CXR and CheXpert datasets. The proposed ensemble of deep convolutional neural networks (DCNN) approach achieves excellent performance with an AUC of 0.9489 for multi-class classification, outperforming many state-of-the-art models.

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Cited By

Anh DHung P(2025)Multi-label Long-Tailed Disease Recognition on Chest X-ray ImagesArtificial Intelligence and Soft Computing10.1007/978-3-031-81596-6_21(231-240)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-81596-6_21
Gopatoti AJayakumar RBilla PPatteeswaran V(2024)DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray imagesJournal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics10.3233/XST-23042132:3(623-649)Online publication date: 6-Apr-2024
https://doi.org/10.3233/XST-230421
Kim JRyu WKim DKim E(2024)Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality mattersScientific Reports10.1038/s41598-024-66530-y14:1Online publication date: 10-Jul-2024
https://doi.org/10.1038/s41598-024-66530-y
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CBMI '22: Proceedings of the 19th International Conference on Content-based Multimedia Indexing

September 2022

208 pages

ISBN:9781450397209

DOI:10.1145/3549555

Copyright © 2022 ACM.

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Published: 07 October 2022

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Compute Canada
WestGrid
New Brunswick Health Research Foundation
New Brunswick Innovation Foundation

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CBMI 2022

CBMI 2022: International Conference on Content-based Multimedia Indexing

September 14 - 16, 2022

Graz, Austria

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Cited By

Anh DHung P(2025)Multi-label Long-Tailed Disease Recognition on Chest X-ray ImagesArtificial Intelligence and Soft Computing10.1007/978-3-031-81596-6_21(231-240)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-81596-6_21
Gopatoti AJayakumar RBilla PPatteeswaran V(2024)DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray imagesJournal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics10.3233/XST-23042132:3(623-649)Online publication date: 6-Apr-2024
https://doi.org/10.3233/XST-230421
Kim JRyu WKim DKim E(2024)Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality mattersScientific Reports10.1038/s41598-024-66530-y14:1Online publication date: 10-Jul-2024
https://doi.org/10.1038/s41598-024-66530-y
R GS MMathivanan SShivahare BChandan RShah M(2024)A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoTScientific Reports10.1038/s41598-024-66427-w14:1Online publication date: 8-Jul-2024
https://doi.org/10.1038/s41598-024-66427-w
Kang HKim NRyu J(2024)Attentional decoder networks for chest X-ray image recognition on high-resolution featuresComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108198251(108198)Online publication date: Jun-2024
https://doi.org/10.1016/j.cmpb.2024.108198
Arslan MHaider AKhurshid MAbu Bakar SJani RMasood FTahir TMitchell KPanchagnula SMandair S(2023)From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical ImagesCureus10.7759/cureus.45587Online publication date: 20-Sep-2023
https://doi.org/10.7759/cureus.45587
Ait Nasser AAkhloufi M(2023)A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using RadiographyDiagnostics10.3390/diagnostics1301015913:1(159)Online publication date: 3-Jan-2023
https://doi.org/10.3390/diagnostics13010159
Souid AAlsubaie NSoufiene BAlqahtani MAbbas MJambi LSakli H(2023)Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approachScientific Reports10.1038/s41598-023-42366-w13:1Online publication date: 3-Oct-2023
https://doi.org/10.1038/s41598-023-42366-w

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