We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach’s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.

Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans / Sarv Ahrabi, S.; Piazzo, L.; Momenzadeh, A.; Scarpiniti, M.; Baccarelli, E.. - In: THE JOURNAL OF SUPERCOMPUTING. - ISSN 0920-8542. - 78:9(2022), pp. 12024-12045. [10.1007/s11227-022-04349-y]

Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans

Sarv Ahrabi S.;Piazzo L.;Momenzadeh A.;Scarpiniti M.
;
Baccarelli E.
2022

Abstract

We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach’s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.
COVID-19; Deep convolutional autoEencoder; Hidden representation; Kernel density estimation; Reconstruction error
01 Pubblicazione su rivista::01a Articolo in rivista
Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans / Sarv Ahrabi, S.; Piazzo, L.; Momenzadeh, A.; Scarpiniti, M.; Baccarelli, E.. - In: THE JOURNAL OF SUPERCOMPUTING. - ISSN 0920-8542. - 78:9(2022), pp. 12024-12045. [10.1007/s11227-022-04349-y]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1650358
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