Evaluation of Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images

Autor:	Simona Condaragiu, Iulian B. Ciocoiu
Rok vydání:	2021
Předmět:	2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) business.industry Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) X ray image Pattern recognition Artificial intelligence Transfer of learning business Convolutional neural network
Zdroj:	2021 International Symposium on Signals, Circuits and Systems (ISSCS).
DOI:	10.1109/isscs52333.2021.9497418
Popis:	The paper presents a comparative analysis of five Convolutional Neural Networks (CNN’s) for reliable detection of COVID-19 from chest X-ray (CXR) images. The transfer learning approach is applied to a dataset compiled from public sources, aiming at discriminating the presence of COVID-19 pathology from normal, lung opacity, and pneumonia situations. Experimental results indicate that best accuracy performances are obtained by the Resnet-50 network, while all CNN’s compare favorably with existing solutions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6275328869e562bc19779f6f63c19599 https://doi.org/10.1109/isscs52333.2021.9497418 Zobrazit plný text záznamu