Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
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DOI：
10.1016/j.cmpb.2020.105608
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发表时间：
2020-11
影响因子：
6.1
通讯作者：
Santone A
中科院分区：
文献类型：
--
作者：
Brunese L;Mercaldo F;Reginelli A;Santone A
背景和目的：冠状病毒病（COVID-19）是一种由以前从未在人类中发现的新病毒引起的传染病。这种病毒会引起呼吸道疾病（例如流感），并出现咳嗽、发烧等症状，严重时还会引发肺炎。检测人类是否存在这种病毒的测试是对痰或血液样本进行的，结果通常会在几个小时或最多几天内得出。分析生物医学成像，患者显示出肺炎迹象。在本文中，为了提供全自动且更快的诊断，我们建议采用深度学习从 X 射线检测 COVID-19。方法：我们特别提出了一种由三个阶段组成的方法：第一个阶段是检测胸部 X 光检查中是否存在肺炎。第二个是区分 COVID-19 和肺炎。最后一步的目的是定位 X 射线中存在 COVID-19 症状的区域。结果与结论：对不同机构的 6,523 幅胸部 X 光片的实验分析证明了该方法的有效性，COVID-19 检测的平均时间约为 2.5 秒，平均准确度等于 0.97。
Background and Objective: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. Method: In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. Results and Conclusion: Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.
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