An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray

Saddam Bekhet, M. Hassaballah, Mourad A. Kenk, Mohamed Abdel Hameed
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引用次数: 23

Abstract

The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.
基于人工智能的胸部x线诊断新冠肺炎技术
2019冠状病毒病大流行对世界卫生和经济造成了灾难性影响。这是由于COVID-19检测试剂盒的局限性导致诊断过程不可避免地延迟。因此,迫切需要建立更便宜和负担得起的诊断方法。胸部x光检查是成功诊断COVID-19的重要第一步,因为它很容易发现任何胸部异常(例如肺部炎症)。此外,大多数医院都有可用于COVID-19早期诊断的x射线设备。然而,放射科医生的短缺是限制COVID-19早期诊断并对治疗过程产生负面影响的关键因素。本文提出了一种基于人工智能的基于医学知识和深度卷积神经网络(cnn)的胸部x线图像早期诊断技术。为此,我们精心构建并微调了深度学习模型,以实现COVID-19检测的最大性能。在最近的基准数据集上的实验结果表明,该技术在识别COVID-19方面具有优异的性能,准确率达到96%。
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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