An overview of deep learning approaches in chest radiograph
Document Type
Article
Publication Date
1-1-2020
Abstract
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.
Keywords
Artificial neural networks, Deep learning, Transfer learning, Multi-task learning, Object detection, Localization, Segmentation
Divisions
sch_ecs,mechanical,1234
Funders
University of Malaya RU Geran, Malaysia ST005-2020
Publication Title
IEEE Access
Volume
8
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA