Abstract
Recently, the National Institutes of Health (NIH) published a chest X-ray image database named “ChestX-ray8”, which contains 108,948 X-ray images that are labeled with eight types of diseases. Identifying the pathologies from the clinical images is a challenging task even for human experts, and to develop computer-aided diagnosis systems to help humans identify the pathologies from images is an urgent need. In this study, we applied the deep learning methods to identify the cardiomegaly from the X-ray images. We tested our algorithms on a dataset containing 600 images, and obtained the best performance with an area under the curve (AUC) of 0.87 using the transfer learning method. This result indicates the feasibility of developing computer-aided diagnosis systems for different pathologies from X-rays using deep learning techniques.
Original language | English (US) |
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Title of host publication | MEDINFO 2019 |
Subtitle of host publication | Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics |
Editors | Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi |
Publisher | IOS Press |
Pages | 482-486 |
Number of pages | 5 |
Volume | 264 |
ISBN (Electronic) | 9781643680026 |
DOIs | |
State | Published - Aug 21 2019 |
Event | 17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019 |
Publication series
Name | Studies in health technology and informatics |
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ISSN (Print) | 0926-9630 |
Conference
Conference | 17th World Congress on Medical and Health Informatics, MEDINFO 2019 |
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Country/Territory | France |
City | Lyon |
Period | 8/25/19 → 8/30/19 |
Bibliographical note
Publisher Copyright:© 2019 International Medical Informatics Association (IMIA) and IOS Press.
Keywords
- Cardiomegaly
- Machine Learning
- X-rays
- Algorithms
- Diagnosis, Computer-Assisted
- Area Under Curve
- Humans
- Deep Learning
PubMed: MeSH publication types
- Journal Article