research-article

Classification of Bacterial and Viral Childhood Pneumonia Using Deep Learning in Chest Radiography

Authors:

Ran YangAuthors Info & Claims

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

Pages 88 - 93

https://doi.org/10.1145/3195588.3195597

Published: 16 March 2018 Publication History

Abstract

Over decades, computer aided diagnosis (CAD) system has been investigated for detection of lung diseases based on chest X-ray images. Incited by the great success of deep learning, in this work, we propose a novel CAD system to identify bacterial and viral pneumonia in chest radiography. The method consists of two parts, lung regions identification and pneumonia category classification. First, left and right lung regions are segmented and extracted with a fully convolutional networks (FCN) model. The model is trained and tested on the open Japanese society of radiological technology database (JSRT, 241 images) and Montgomery County, Md (MC, 138 images) dataset. After segmentation, a deep convolutional neural network (DCNN) model is used to classify the target lung regions. Then, based on the DCNN model, features of the target lung regions are extracted automatically and the performance is compared with that of manual features. Finally, the DCNN features and manual features are fused together and are put into support vector machines (SVM) classifier for binary classification. The proposed method is evaluated on a dataset of Guangzhou Women and Children's Medical Center, China, with 4,513 pediatric patients in total, aged from 1 to 9 years old, during the period from 2003 to 2017. The performances are measured by different criteria: accuracy, precision, sensitivity, specificity and area under the curve (AUC), which is a comprehensive criterion. The experimental results showed better accuracy (0.8048±0.0202) and sensitivity (0.7755±0.0296) in extracting features by DCNN with transfer learning. The values of AUC varied from 0.6937 to 0.8234. And an ensemble of different kinds of features slightly improved the AUC value from 0.8160±0.0162 to 0.8234±0.0014.

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Cited By

R PGajendran GBoulaaras STantawy S(2025)PediaPulmoDx: Harnessing Cutting Edge Preprocessing and Explainable AI for Pediatric Chest X-ray Classification with DenseNet121Results in Engineering10.1016/j.rineng.2025.104320(104320)Online publication date: Feb-2025
https://doi.org/10.1016/j.rineng.2025.104320
Patidar MPandey GKoolagudi SS KChandra V(2024)Enhancing Paediatric Healthcare: Deep Learning-Based Pneumonia Diagnosis from Children's Chest X-raysProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676041(128-135)Online publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1145/3675888.3676041
Siddique ABoulila WAlshehri MAhmed FGadekallu TVictor NQadri MAhmad J(2024)Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0IEEE Transactions on Consumer Electronics10.1109/TCE.2023.331956570:1(1923-1939)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3319565
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Index Terms

Classification of Bacterial and Viral Childhood Pneumonia Using Deep Learning in Chest Radiography
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

ICMIP '18: Proceedings of the 3rd International Conference on Multimedia and Image Processing

March 2018

125 pages

ISBN:9781450364683

DOI:10.1145/3195588

Copyright © 2018 ACM.

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Wuhan Univ.: Wuhan University, China
University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

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Publication History

Published: 16 March 2018

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ICMIP 2018: 2018 the 3rd International Conference on Multimedia and Image Processing

March 16 - 18, 2018

Guiyang, China

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Cited By

R PGajendran GBoulaaras STantawy S(2025)PediaPulmoDx: Harnessing Cutting Edge Preprocessing and Explainable AI for Pediatric Chest X-ray Classification with DenseNet121Results in Engineering10.1016/j.rineng.2025.104320(104320)Online publication date: Feb-2025
https://doi.org/10.1016/j.rineng.2025.104320
Patidar MPandey GKoolagudi SS KChandra V(2024)Enhancing Paediatric Healthcare: Deep Learning-Based Pneumonia Diagnosis from Children's Chest X-raysProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676041(128-135)Online publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1145/3675888.3676041
Siddique ABoulila WAlshehri MAhmed FGadekallu TVictor NQadri MAhmad J(2024)Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0IEEE Transactions on Consumer Electronics10.1109/TCE.2023.331956570:1(1923-1939)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3319565
Aditya Pai HPareek PGuruprasad MChandrappa SBhargava K(2024)Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep Learning2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)10.1109/NMITCON62075.2024.10699169(1-6)Online publication date: 9-Aug-2024
https://doi.org/10.1109/NMITCON62075.2024.10699169
Gahlot MGhosh P(2024)Pneumonia Detection and Chest X-Rays: Comprehensive Analysis of Artificial Intelligence Techniques in Clinical and Radiological Insights2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)10.1109/ICoICI62503.2024.10696161(1194-1200)Online publication date: 28-Aug-2024
https://doi.org/10.1109/ICoICI62503.2024.10696161
Mahapackialakshmi KJaffino G(2024)SIFT-AD: Scale-Invariant Feature Transform for Automatic Detection of Lung Diseases in X-ray Images2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574554(1-6)Online publication date: 3-May-2024
https://doi.org/10.1109/AIIoT58432.2024.10574554
S.N KS KZafar SKumar H(2024)ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detectionHeliyon10.1016/j.heliyon.2024.e2779810:6(e27798)Online publication date: Mar-2024
https://doi.org/10.1016/j.heliyon.2024.e27798
Rani SKaur N(2024)Comparative Analysis of Pneumonia Detection from Chest X-ray Using Deep LearningThe Future of Artificial Intelligence and Robotics10.1007/978-3-031-60935-0_25(276-287)Online publication date: 20-Aug-2024
https://doi.org/10.1007/978-3-031-60935-0_25
Saboo YKapse SPrasanna P(2023)Convolutional Neural Networks (CNNs) for Pneumonia Classification on Pediatric Chest RadiographsCureus10.7759/cureus.44130Online publication date: 25-Aug-2023
https://doi.org/10.7759/cureus.44130
Hedhoud YMekhaznia TAmroune M(2023)An improvement of the CNN-XGboost model for pneumonia disease classificationPolish Journal of Radiology10.5114/pjr.2023.13253388(483-493)Online publication date: 25-Oct-2023
https://doi.org/10.5114/pjr.2023.132533
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