An overview of deep learning approaches in chest radiograph

Anis, Shazia and Lai, Khin Wee and Chuah, Joon Huang and Ali, Shoaib Mohammad and Mohafez, Hamidreza and Hadizadeh, Maryam and Yan, Ding and Ong, Zhi-Chao (2020) An overview of deep learning approaches in chest radiograph. IEEE Access, 8. pp. 182347-182354. ISSN 2169-3536, DOI

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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.

Item Type: Article
Funders: University of Malaya RU Geran, Malaysia ST005-2020
Uncontrolled Keywords: Artificial neural networks; Deep learning; Transfer learning; Multi-task learning; Object detection; Localization; Segmentation
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Engineering > Department of Electrical Engineering
Faculty of Engineering > Department of Mechanical Engineering
Faculty of Sports and Exercise Science (formerly known as Centre for Sports & Exercise Sciences)
Depositing User: Ms Zaharah Ramly
Date Deposited: 14 Nov 2023 01:15
Last Modified: 14 Nov 2023 01:15

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