Evolution of Artificial Intelligence in Bone Fracture Detection

Deepti Mishra, G. Bajaj
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引用次数: 0

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.
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人工智能在骨折检测中的发展
本文的目的是介绍基于深度学习的人工智能技术,该技术可用于在x射线上检测骨骼骨折。这篇论文包括对各种实体的讨论。首先,对数据的形成和处理进行了讨论。然后,介绍了用于裂缝检测的不同图像处理技术。随后,分析了传统的和当前的神经网络裂缝检测方法。并对其进行了比较分析。最后,本文对裂缝检测研究人员面临的问题和挑战进行了讨论。研究表明,深度学习技术比传统的x射线骨折检测方法在诊断方面更准确。这篇论文为研究人员在使用深度学习技术时处理x射线骨折检测遇到的困难和问题提供了一条途径。
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CiteScore
3.20
自引率
0.00%
发文量
43
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