用TensorFlow和卷积神经网络检测肺炎

Dejan Babic, I. Jovović, Tomo Popović, Stevan Cakic, Luka Filipovic
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引用次数: 2

摘要

人工智能越来越多地参与到我们的日常生活中,因为有大量的数据可供机器和深度学习算法使用。深度学习为医学科学的应用引入了新的维度和可能性。随着2020年COVID-19在全球范围内的爆发,许多国家的卫生系统不堪重负。由于许多患者受到感染,卫生系统面临着正确诊断患者病情的压力。在许多情况下,由于许多地区的许多疫情,许多新冠病毒阳性的肺炎患者几乎无法正确诊断。可以确定地检测肺炎的智能系统可以帮助缓解卫生系统的压力,并使医生专注于病情更严重的患者。本文描述了肺炎检测模型的开发,利用TensorFlow对胸部x线图像进行处理,以确定患者是否患有肺炎。该模型基于卷积神经网络支持的深度学习算法。本文提出的模型在分析x射线图像方面达到了相当高的准确度(95%以上),可以用于加快医疗保健决策过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Pneumonia With TensorFlow and Convolutional Neural Networks
Artificial intelligence is getting more and more involved in our everyday life as a result of enormous amounts of data available for feeding the machine and deep learning algorithms. Deep learning introduced new dimensions and possibilities of applications in medical science. With COVID-19 outbreak in 2020 at global level, the health systems of many countries were overwhelmed. With many patients infected, health system is pressured to correctly diagnose patient’s state of illness. In a lot of occasions, it was almost impossible to correctly diagnose many COVID-19 positive patients that have pneumonia due to many outbreaks in many areas. The intelligent system that could detect pneumonia with certainty could help in easing the pressure on the health system and make doctors focus on more severely ill patients. This paper describes development of pneumonia detection model using TensorFlow to processes the chest X-ray images to determine whether the patient has pneumonia. The model is based on deep learning algorithm supported through convolutional neural network. The model presented in this paper has achieved rather high accuracy (over 95%) in analyzing X-Ray images and could be used to speed up decision process in healthcare.
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