利用计算机视觉识别肺部感染患者的x线图像

M. Mahyoub, Thomas Coombs, M. Jayabalan, J. Mustafina, A. Hussain
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引用次数: 0

摘要

本研究提出了一种基于计算机视觉的解决方案,以相当或更好的精度识别患者是否感染了covid - 19/正常/肺炎。提出的解决方案基于深度学习技术CNN(卷积神经网络),采用多种方法覆盖所有开放问题。第一种方法是基于CNN模型的预训练模型;第二种方法是从头开始创建CNN模型。对多种方法的试验和评估有助于覆盖为解决这一问题而进行的相关工作中所有未注意到的开放点和空白。根据两种方法的实验结果和其他研究者相关工作的研究,两种方法都是同样有效的,可以推荐用于肺部疾病的多类分类。
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Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision
This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.
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