An Overview of Deep Learning Approaches in Chest Radiograph
Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital medical conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure and bone fr...
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IEEE
2020
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oai:doaj.org-article:5a7956dbd8a84f8e8d2357699919d4372021-11-19T00:06:14ZAn Overview of Deep Learning Approaches in Chest Radiograph2169-353610.1109/ACCESS.2020.3028390https://doaj.org/article/5a7956dbd8a84f8e8d2357699919d4372020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9211471/https://doaj.org/toc/2169-3536Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital medical conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure and bone fracture. The current practices often involve tedious manual processes dependent on the expertise of radiologist or consultant, thus, the execution is easily prone to human errors of being misdiagnosed. With the recent advances of deep learning and increased hardware computational power, researchers are working on various networks and algorithms to develop machines learning that can assists radiologists in their diagnosis and reduce the probability of misdiagnosis. This paper presents a review of deep learning advancements made in the field of chest radiography. It discusses single and multi-level localization and segmentation techniques adopted by researchers for higher accuracy and precision.Shazia AnisKhin Wee LaiJoon Huang ChuahShoaib Mohammad AliHamidreza MohafezMaryam HadizadehDing YanZhi-Chao OngIEEEarticleArtificial neural networksdeep learningtransfer learningmulti-task learningobject detectionlocalizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 182347-182354 (2020) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Artificial neural networks deep learning transfer learning multi-task learning object detection localization Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| spellingShingle |
Artificial neural networks deep learning transfer learning multi-task learning object detection localization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shazia Anis Khin Wee Lai Joon Huang Chuah Shoaib Mohammad Ali Hamidreza Mohafez Maryam Hadizadeh Ding Yan Zhi-Chao Ong An Overview of Deep Learning Approaches in Chest Radiograph |
| description |
Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital medical conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure and bone fracture. The current practices often involve tedious manual processes dependent on the expertise of radiologist or consultant, thus, the execution is easily prone to human errors of being misdiagnosed. With the recent advances of deep learning and increased hardware computational power, researchers are working on various networks and algorithms to develop machines learning that can assists radiologists in their diagnosis and reduce the probability of misdiagnosis. This paper presents a review of deep learning advancements made in the field of chest radiography. It discusses single and multi-level localization and segmentation techniques adopted by researchers for higher accuracy and precision. |
| format |
article |
| author |
Shazia Anis Khin Wee Lai Joon Huang Chuah Shoaib Mohammad Ali Hamidreza Mohafez Maryam Hadizadeh Ding Yan Zhi-Chao Ong |
| author_facet |
Shazia Anis Khin Wee Lai Joon Huang Chuah Shoaib Mohammad Ali Hamidreza Mohafez Maryam Hadizadeh Ding Yan Zhi-Chao Ong |
| author_sort |
Shazia Anis |
| title |
An Overview of Deep Learning Approaches in Chest Radiograph |
| title_short |
An Overview of Deep Learning Approaches in Chest Radiograph |
| title_full |
An Overview of Deep Learning Approaches in Chest Radiograph |
| title_fullStr |
An Overview of Deep Learning Approaches in Chest Radiograph |
| title_full_unstemmed |
An Overview of Deep Learning Approaches in Chest Radiograph |
| title_sort |
overview of deep learning approaches in chest radiograph |
| publisher |
IEEE |
| publishDate |
2020 |
| url |
https://doaj.org/article/5a7956dbd8a84f8e8d2357699919d437 |
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