Open AccessAnalysis of identifying COVID-19 with deep learning model
Author(s) -
Bin Fan,
Hai Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/5/052021
Subject(s) - deep learning , covid-19 , artificial intelligence , computer science , sample (material) , feature extraction , machine learning , test (biology) , deep neural networks , medicine , disease , virology , pathology , infectious disease (medical specialty) , geology , chromatography , outbreak , paleontology , chemistry
Coronary Virus Disease 2019 swept the world and caused serious impact on human society. Doctors usually use CT scan pictures and chest X-ray images to determine whether a patient is infected. Many researchers try to use deep learning methods to test COVID-19 of patients. However, there are many problems when using deep learning methods for feature extraction, such as: fewer data samples, unclear pictures, and pictures containing special marks. This article uses deep learning methods for COVID-19 detection and visual analysis of popular deep learning methods. Experiments verify that when using deep learning in the public small sample COVID-19 dataset, a small part of the test results are not reliable. We propose solutions to the problems of deep learning during COVID-19 detection.