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Title: A literature review on deep learning algorithms for analysis of X-ray images
Authors: Seyfi, Gökhan
Esme, Engin
Yılmaz, Merve
Kıran, Mustafa Servet
Keywords: Deep learning
X-ray image
Object detection
Anomaly Detection
Issue Date: 2023
Abstract: Since the invention of the X-ray beam, it has been used for useful applications in various fields, such as medical diagnosis, fluoroscopy, radiation therapy, and computed tomography. In addition, it is also widely used to identify prohibited or illegal materials using X-ray imaging in the security field. However, these procedures are generally dependent on the human factor. An operator detects prohibited objects by projecting pseudo-color images onto a computer screen. Because these processes are prone to error, much work has gone into automating the processes involved. Initial research on this topic consisted mainly of machine learning and methods using hand-crafted features. The newly developed deep learning methods have subsequently been more successful. For this reason, deep learning algorithms are a trend in recent studies and the number of publications has increased in areas such as X-ray imaging. Therefore, we surveyed the studies published in the literature on Deep Learning-based X-ray imaging to attract new readers and provide new perspectives.
ISSN: 1868-8071
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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