Evolution of Artificial Intelligence in Bone Fracture Detection

Deepti Mishra, Amit Kumar Mishra

Source Title: International Journal of Reliable and Quality E-Healthcare (IJRQEH)11(2)

ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781683182580|DOI: 10.4018/IJRQEH.299958

MLA

Mishra, Deepti, and Amit Kumar Mishra. "Evolution of Artificial Intelligence in Bone Fracture Detection." IJRQEH vol.11, no.2 2022: pp.1-17. http://doi.org/10.4018/IJRQEH.299958

APA

Mishra, D. & Mishra, A. K. (2022). Evolution of Artificial Intelligence in Bone Fracture Detection. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 11(2), 1-17. http://doi.org/10.4018/IJRQEH.299958

Chicago

Mishra, Deepti, and Amit Kumar Mishra. "Evolution of Artificial Intelligence in Bone Fracture Detection," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 11, no.2: 1-17. http://doi.org/10.4018/IJRQEH.299958

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Abstract

The objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of discussions of various entities. Initially, there is a discussion on data formulation and processing. Following which, distinguished image processing techniques are presented for fracture detection. Later, there is an analysis of conventional and current neural network methodologies for fracture detection techniques. Furthermore, there is a comparative analysis for the same. Finally, in the end, a discussion is presented in the paper regarding problems and challenges confronted by researchers for fracture detection. The study shows, deep learning techniques provide accuracy in the diagnosis than the conventional methods in fracture detection on X-rays. The paper leads to a path for the researchers to deal with difficulties and issues encountered with the fracture detection on X-rays while using deep learning techniques.