Department of Electrical and Electronic Engineering Near East University Nicosia, North Cyprus Via Mersin 10, Turkey.
Department of Electrical and Electronic Engineering Near East University Nicosia, North Cyprus Via Mersin 10, Turkey.
Comput Biol Med. 2021 May;132:104306. doi: 10.1016/j.compbiomed.2021.104306. Epub 2021 Mar 10.
A new pneumonia-type coronavirus, COVID-19, recently emerged in Wuhan, China. COVID-19 has subsequently infected many people and caused many deaths worldwide. Isolating infected people is one of the methods of preventing the spread of this virus. CT scans provide detailed imaging of the lungs and assist radiologists in diagnosing COVID-19 in hospitals. However, a person's CT scan contains hundreds of slides, and the diagnosis of COVID-19 using such scans can lead to delays in hospitals. Artificial intelligence techniques could assist radiologists with rapidly and accurately detecting COVID-19 infection from these scans. This paper proposes an artificial intelligence (AI) approach to classify COVID-19 and normal CT volumes. The proposed AI method uses the ResNet-50 deep learning model to predict COVID-19 on each CT image of a 3D CT scan. Then, this AI method fuses image-level predictions to diagnose COVID-19 on a 3D CT volume. We show that the proposed deep learning model provides 96% AUC value for detecting COVID-19 on CT scans.
一种新型肺炎型冠状病毒，COVID-19，最近在中国武汉出现。COVID-19 随后感染了许多人，并在全球范围内造成了许多人死亡。隔离感染者是预防这种病毒传播的方法之一。CT 扫描提供了肺部的详细图像，并帮助放射科医生在医院诊断 COVID-19。然而，一个人的 CT 扫描包含数百张幻灯片，使用这种扫描来诊断 COVID-19 可能导致医院的延误。人工智能技术可以帮助放射科医生从这些扫描中快速准确地检测 COVID-19 感染。本文提出了一种人工智能（AI）方法来对 COVID-19 和正常 CT 体积进行分类。所提出的 AI 方法使用 ResNet-50 深度学习模型来预测 3D CT 扫描中每个 CT 图像上的 COVID-19。然后，该 AI 方法融合图像级预测以在 3D CT 体积上诊断 COVID-19。我们表明，所提出的深度学习模型在 CT 扫描上检测 COVID-19 的 AUC 值为 96%。
