Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million people around the world. The aim of this work is to provide a Covid-19 infection screening tool. Currently, the most widely used clinical tool for detecting the presence of infection is the reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less sensitive and requires the resource of specialized medical personnel. The use of X-ray images represents one of the latest challenges for the rapid diagnosis of the Covid-19 infection. This work involves the use of advanced artificial intelligence techniques for diagnosis using algorithms for classification purposes. The goal is to provide an automatic infection detection method while maximizing detection accuracy. A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99.3% vs 85.58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

Deep Learning algorithms for automatic COVID-19 detection on chest X-ray images / Cannata, Sergio; Paviglianiti, Annunziata; Pasero, Eros; Cirrincione, Giansalvo; Cirrincione, Maurizio. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 10:(2022), pp. 119905-119913. [10.1109/ACCESS.2022.3221531]

Deep Learning algorithms for automatic COVID-19 detection on chest X-ray images

Cannata, Sergio;Paviglianiti, Annunziata;Pasero, Eros;Cirrincione, Giansalvo;
2022

Abstract

Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million people around the world. The aim of this work is to provide a Covid-19 infection screening tool. Currently, the most widely used clinical tool for detecting the presence of infection is the reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less sensitive and requires the resource of specialized medical personnel. The use of X-ray images represents one of the latest challenges for the rapid diagnosis of the Covid-19 infection. This work involves the use of advanced artificial intelligence techniques for diagnosis using algorithms for classification purposes. The goal is to provide an automatic infection detection method while maximizing detection accuracy. A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99.3% vs 85.58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2973052