Coronavirus Disease 2019, is an infectious respiratory disease caused by the virus called SARS-CoV-2 which in November 2020 recorded more than 60 million cases and approximately 1.45 million deaths worldwide. The most widely used diagnostic test is the Reverse transcriptase-polymerase chain reaction (RT-PCR), accompanied by chest X-Rays (CXR) designated as the main diagnostic tool in high-risk areas and for symptomatic subjects awaiting response from the RT-PCR test for which up to 24 hours are required. Although CXR is a quick and valid diagnostic test, its discrimination against viral pneumonia is the main clinical challenge, due to the similar lung dam- age that is reported in alveolar tissue in the early stages of COVID-19. In this study, has been proposed an automated algorithm for detecting COVID-19 lung damages from CXR images using ResNet convolutional neural networks: ResNet-18 and ResNet-34. In particular the supervised learning technique has been followed in order to learn the network in the COVID-19 and viral pneumonia recognition using the CXR images database present in the Kaggle CXR image repository. After the training both the networks have been tested on a test dataset providing the following results: accuracy of 98.33%, precision of 100.00%, sensitivity of 96.67%, specificity of 100% and F1-score of 0.98 for the ResNet-18. The results of the ResNet-34 have been the following ones: accuracy of 95.00%, precision of 100.00%, sensitivity of 90.00%, specificity of 100% and F1-score of 0.95 for the ResNet-34. In conclusion the ResNet-18 provided better results of the ResNet-34 resulting the best candidate for the COVID-19 lung damage detection and discrimination from viral pneumonia being an optimum tool for overcoming the main clinical challenge.

Coronavirus Disease 2019, is an infectious respiratory disease caused by the virus called SARS-CoV-2 which in November 2020 recorded more than 60 million cases and approximately 1.45 million deaths worldwide. The most widely used diagnostic test is the Reverse transcriptase-polymerase chain reaction (RT-PCR), accompanied by chest X-Rays (CXR) designated as the main diagnostic tool in high-risk areas and for symptomatic subjects awaiting response from the RT-PCR test for which up to 24 hours are required. Although CXR is a quick and valid diagnostic test, its discrimination against viral pneumonia is the main clinical challenge, due to the similar lung dam- age that is reported in alveolar tissue in the early stages of COVID-19. In this study, has been proposed an automated algorithm for detecting COVID-19 lung damages from CXR images using ResNet convolutional neural networks: ResNet-18 and ResNet-34. In particular the supervised learning technique has been followed in order to learn the network in the COVID-19 and viral pneumonia recognition using the CXR images database present in the Kaggle CXR image repository. After the training both the networks have been tested on a test dataset providing the following results: accuracy of 98.33%, precision of 100.00%, sensitivity of 96.67%, specificity of 100% and F1-score of 0.98 for the ResNet-18. The results of the ResNet-34 have been the following ones: accuracy of 95.00%, precision of 100.00%, sensitivity of 90.00%, specificity of 100% and F1-score of 0.95 for the ResNet-34. In conclusion the ResNet-18 provided better results of the ResNet-34 resulting the best candidate for the COVID-19 lung damage detection and discrimination from viral pneumonia being an optimum tool for overcoming the main clinical challenge.

Detecting COVID-19 Lung Damages from Chest X-Rays Images by AI-based Algorithm

COLELLA, EMANUEL
2019/2020

Abstract

Coronavirus Disease 2019, is an infectious respiratory disease caused by the virus called SARS-CoV-2 which in November 2020 recorded more than 60 million cases and approximately 1.45 million deaths worldwide. The most widely used diagnostic test is the Reverse transcriptase-polymerase chain reaction (RT-PCR), accompanied by chest X-Rays (CXR) designated as the main diagnostic tool in high-risk areas and for symptomatic subjects awaiting response from the RT-PCR test for which up to 24 hours are required. Although CXR is a quick and valid diagnostic test, its discrimination against viral pneumonia is the main clinical challenge, due to the similar lung dam- age that is reported in alveolar tissue in the early stages of COVID-19. In this study, has been proposed an automated algorithm for detecting COVID-19 lung damages from CXR images using ResNet convolutional neural networks: ResNet-18 and ResNet-34. In particular the supervised learning technique has been followed in order to learn the network in the COVID-19 and viral pneumonia recognition using the CXR images database present in the Kaggle CXR image repository. After the training both the networks have been tested on a test dataset providing the following results: accuracy of 98.33%, precision of 100.00%, sensitivity of 96.67%, specificity of 100% and F1-score of 0.98 for the ResNet-18. The results of the ResNet-34 have been the following ones: accuracy of 95.00%, precision of 100.00%, sensitivity of 90.00%, specificity of 100% and F1-score of 0.95 for the ResNet-34. In conclusion the ResNet-18 provided better results of the ResNet-34 resulting the best candidate for the COVID-19 lung damage detection and discrimination from viral pneumonia being an optimum tool for overcoming the main clinical challenge.
2019
2020-12-17
Detecting COVID-19 Lung Damages from Chest X-Rays Images by AI-based Algorithm
Coronavirus Disease 2019, is an infectious respiratory disease caused by the virus called SARS-CoV-2 which in November 2020 recorded more than 60 million cases and approximately 1.45 million deaths worldwide. The most widely used diagnostic test is the Reverse transcriptase-polymerase chain reaction (RT-PCR), accompanied by chest X-Rays (CXR) designated as the main diagnostic tool in high-risk areas and for symptomatic subjects awaiting response from the RT-PCR test for which up to 24 hours are required. Although CXR is a quick and valid diagnostic test, its discrimination against viral pneumonia is the main clinical challenge, due to the similar lung dam- age that is reported in alveolar tissue in the early stages of COVID-19. In this study, has been proposed an automated algorithm for detecting COVID-19 lung damages from CXR images using ResNet convolutional neural networks: ResNet-18 and ResNet-34. In particular the supervised learning technique has been followed in order to learn the network in the COVID-19 and viral pneumonia recognition using the CXR images database present in the Kaggle CXR image repository. After the training both the networks have been tested on a test dataset providing the following results: accuracy of 98.33%, precision of 100.00%, sensitivity of 96.67%, specificity of 100% and F1-score of 0.98 for the ResNet-18. The results of the ResNet-34 have been the following ones: accuracy of 95.00%, precision of 100.00%, sensitivity of 90.00%, specificity of 100% and F1-score of 0.95 for the ResNet-34. In conclusion the ResNet-18 provided better results of the ResNet-34 resulting the best candidate for the COVID-19 lung damage detection and discrimination from viral pneumonia being an optimum tool for overcoming the main clinical challenge.
File in questo prodotto:
File Dimensione Formato  
MT_Emanuel_Colella.pdf

Open Access dal 17/12/2022

Dimensione 17.24 MB
Formato Adobe PDF
17.24 MB Adobe PDF Visualizza/Apri

I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12075/4679