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To this end, we design and train, in an <jats:italic>unsupervised<\/jats:italic> manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed <jats:italic>only<\/jats:italic> 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\u2019s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11227-022-04349-y","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T12:03:01Z","timestamp":1645704181000},"page":"12024-12045","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploiting probability density function of deep convolutional autoencoders\u2019 latent space for reliable COVID-19 detection on CT scans"],"prefix":"10.1007","volume":"78","author":[{"given":"Sima","family":"Sarv Ahrabi","sequence":"first","affiliation":[]},{"given":"Lorenzo","family":"Piazzo","sequence":"additional","affiliation":[]},{"given":"Alireza","family":"Momenzadeh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3164-6256","authenticated-orcid":false,"given":"Michele","family":"Scarpiniti","sequence":"additional","affiliation":[]},{"given":"Enzo","family":"Baccarelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"4349_CR1","doi-asserted-by":"publisher","unstructured":"Ariana Axiaq, Ahmad Almohtadi, Samuel\u00a0A Massias, et\u00a0al (2021) The role of computed tomography scan in the diagnosis of COVID-19 pneumonia. 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