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AI-Based Classification Algorithm of Infrared Images of Patients with Spinal Disorders

机译:基于AI的脊柱障碍患者红外图像分类算法

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Infrared thermal imaging is a non-destructive, non-invasive technique that has shown to be effective in the detection and pre-clinical diagnosis of a variety of disorders. Nowadays, some medical applications have already been successfully implemented in pre-clinic diagnostics using thermography based on AI algorithms to support decision-based medical tasks. Though, the massive amount of image types, disease variety, and numerous individual anatomical features of the human body continue to give researchers more challenging jobs that still need to be solved. This paper proposes a novel methodology using a convolutional neural network (CNN) for analyzing with high accuracy infrared thermal images from the spine region for quick screening and disease classification of patients.
机译:红外线热成像是一种非破坏性,无侵入性技术,证明在检测和前临床诊断方面有效的各种疾病。 如今,使用基于AI算法的热定貌在临床前诊断中已经成功地实施了一些医疗应用,以支持基于决策的医疗任务。 虽然,人体的大量图像类型,疾病品种和众多单独解剖特征继续为研究人员提供更具挑战性的工作,但仍需要解决。 本文提出了一种利用卷积神经网络(CNN)的新方法,用于分析来自脊柱区域的高精度红外热图像,以便快速筛查和疾病的患者分类。

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