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Convolutional Neural Network Techniques on X-ray Images for Covid-19 Classification

Contributo in Atti di convegno
Data di Pubblicazione:
2021
Abstract:
At the end of 2019, the World Health Organization (WHO) referred that the Public Health Commission of Hubei Province, China, reported cases of severe and unknown pneumonia. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, called COVID-19 (coronavirus disease 2019). An early diagnosis of those carrying the virus becomes crucial to contain the spread, morbidity and mortality of the pandemic. The definitive diagnosis is made through specific tests, among which imaging tests play a very important role. Achieving this goal cannot be separated from radiological examination, and chest X-ray is the most easily available and least expensive alternative. The use of X-ray chest radiographs, as an element that assists the diagnosis and that allows the follow up of the disease, is the subject of many publications that adopt machine learning approaches. This work focuses on the most adopted Convolutional Neural Network Techniques applied on chest X-ray images.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Convolutional Neural Networks; COVID-19 diagnose; X-ray image Classification
Elenco autori:
Vocaturo, E.; Zumpano, E.; Caroprese, L.
Autori di Ateneo:
CAROPRESE Luciano
Link alla scheda completa:
https://ricerca.unich.it/handle/11564/794979
Titolo del libro:
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
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