COVID-19 Detection Using Raw Chest X-Ray Images

M. Shukla, A. Rasool, Aditya Jain, Vishwas Sahu, Prerak Verma, A. Dubey
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Abstract

COVID-19 has had a lasting effect on the human population around the globe. originating from Wuhan, China, in December 2019, the virus managed to spread worldwide in a short time. Huge waiting time between the detection of symptoms and clinical confirmation of the virus being present in the body has made the virus more fatal; thus, rapid screening of large numbers of suspected patients is essential. Due to inefficiency in pathological testing, alternate ways must be devised to combat these issues. Due to advancements in CAD, integrating radiological images with Artificial Intelligence (AI) can detect the disease accurately. This study proposes a deep learning model for automatic COVID-19 detection using raw Chest X-ray (CXR) images. With 17 convolutional layers, the proposed model is trained to diagnose COVID-19 with an 96.67% accuracy. The model can be used to help the world in numerous ways.
使用原始胸部x射线图像检测COVID-19
COVID-19对全球人口产生了持久影响。该病毒于2019年12月起源于中国武汉,并在短时间内传播到世界各地。从发现症状到临床确认病毒在体内存在之间的漫长等待时间,使得该病毒更具致命性;因此,对大量疑似患者进行快速筛查至关重要。由于病理检测效率低下,必须设计替代方法来解决这些问题。由于CAD的进步,将放射图像与人工智能(AI)相结合可以准确地检测疾病。本研究提出了一种深度学习模型,用于使用原始胸部x射线(CXR)图像自动检测COVID-19。通过17个卷积层,该模型被训练成诊断COVID-19的准确率为96.67%。这个模型可以在很多方面帮助世界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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