At the end of 2019 anew coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, now called COVID-19 (coronavirus disease 2019). Since then there has been an exponential growth of infections and at the beginning of March 2020 the WHO declared the epidemic a global emergency. 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 an important role in the care path of the patient with suspected or confirmed COVID-19. Patients with serious COVID-19 typically experience viral pneumonia. This paper uses the Multiple Instance Learning paradigm to classify pneumonia X-ray images, considering three different classes: radiographies of healthy people, radiographies of people with bacterial pneumonia and of people with viral pneumonia. The proposed algorithms, which are very fast in practice, appear promising especially if we take into account that no preprocessing technique has been used.

Multiple Instance Learning for Viral Pneumonia Chest X-ray Classification

Avolio, Matteo;Fuduli, Antonio;Vocaturo, Eugenio
;
Zumpano, Ester
2022-01-01

Abstract

At the end of 2019 anew coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, now called COVID-19 (coronavirus disease 2019). Since then there has been an exponential growth of infections and at the beginning of March 2020 the WHO declared the epidemic a global emergency. 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 an important role in the care path of the patient with suspected or confirmed COVID-19. Patients with serious COVID-19 typically experience viral pneumonia. This paper uses the Multiple Instance Learning paradigm to classify pneumonia X-ray images, considering three different classes: radiographies of healthy people, radiographies of people with bacterial pneumonia and of people with viral pneumonia. The proposed algorithms, which are very fast in practice, appear promising especially if we take into account that no preprocessing technique has been used.
Pneumonia imaging Classification, Multiple Instance Learning, Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/342343
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