Identifying Drug-Resistant Tuberculosis in Chest Radiographs : Evaluation of CNN Architectures and Training Strategies
Tuberculosis (TB) is a serious infectious disease that mainly affects the lungs. Drug resistance to the disease makes it more challenging to control. Early diagnosis of drug resistance can help with decision making resulting in appropriate and successful treatment. Chest X-rays (CXRs) have been pivotal to identifying tuberculosis and are widely available. In this work, we utilize CXRs to distinguish between drug-resistant and drug-sensitive tuberculosis. We incorporate Convolutional Neural Network (CNN) based models to discriminate the two types of TB, and employ standard and deep learning based data augmentation methods to improve the classification. Using labeled data from NIAID TB Portals and additional non-labeled sources, we were able to achieve an Area Under the ROC Curve (AUC) of up to 85% using a pretrained InceptionV3 network.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2021 |
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| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2021(2021) vom: 17. Nov., Seite 2964-2967 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Karki, Manohar [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 30.12.2021 Date Revised 30.12.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.1109/EMBC46164.2021.9630189 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM334283957 |
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| 520 | |a Tuberculosis (TB) is a serious infectious disease that mainly affects the lungs. Drug resistance to the disease makes it more challenging to control. Early diagnosis of drug resistance can help with decision making resulting in appropriate and successful treatment. Chest X-rays (CXRs) have been pivotal to identifying tuberculosis and are widely available. In this work, we utilize CXRs to distinguish between drug-resistant and drug-sensitive tuberculosis. We incorporate Convolutional Neural Network (CNN) based models to discriminate the two types of TB, and employ standard and deep learning based data augmentation methods to improve the classification. Using labeled data from NIAID TB Portals and additional non-labeled sources, we were able to achieve an Area Under the ROC Curve (AUC) of up to 85% using a pretrained InceptionV3 network | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Kantipudi, Karthik |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Hang |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Kassim, Yasmin M |e verfasserin |4 aut | |
| 700 | 1 | |a Yaniv, Ziv |e verfasserin |4 aut | |
| 700 | 1 | |a Jaeger, Stefan |e verfasserin |4 aut | |
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