Author: Mittal, B.; Oh, J.
Title: CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images Cord-id: 3ys2t6u9 Document date: 2021_1_1
ID: 3ys2t6u9
Snippet: The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus – 2 (SARS-CoV-2). Over 146 million cases and 3.1 million deaths were reported worldwide as of April 27, 2021. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for the early diagnosis of Covid-19. However, this classification involves radiologists’ time and efforts significantly. It is crucial to develop an auto
Document: The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus – 2 (SARS-CoV-2). Over 146 million cases and 3.1 million deaths were reported worldwide as of April 27, 2021. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for the early diagnosis of Covid-19. However, this classification involves radiologists’ time and efforts significantly. It is crucial to develop an automated analysis of CT images to save their time and efforts. In this paper, we propose CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose Covid-19 from CT images. We trained and tested the proposed CoviNet using two public datasets with radiologist-labeled CT images. The experimental results show the proposed CoviNet is promising. © 2021 SPIE.
Search related documents:
Co phrase search for related documents- Try single phrases listed below for: 1
Co phrase search for related documents, hyperlinks ordered by date