A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by...

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Published in:	Expert systems with applications Vol. 192; p. 116366
Main Authors:	Scarpiniti, Michele, Sarv Ahrabi, Sima, Baccarelli, Enzo, Piazzo, Lorenzo, Momenzadeh, Alireza
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 15.04.2022 Elsevier BV
Subjects:	Algorithms Anomaly detection Chest Computed tomography Computed Tomography (CT) Convolutional autoencoders Coronavirus disease 2019 Coronaviruses COVID-19 Deep denoising autoencoder Exploitation Feature learning Histograms Inspection Medical imaging Noise reduction Pneumonia Representations Viral diseases COVID-19 Pneumonia Convolutional autoencoders Computed Tomography (CT) Coronavirus disease 2019 Deep denoising autoencoder Feature learning Anomaly detection
ISSN:	0957-4174, 1873-6793, 0957-4174
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Abstract	Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost. •Unsupervised high-performance automatic COVID-19 disease detection.•Deep Denoising Convolutional Autoencoders for CT scans classification.•Distance-based hidden feature robust statistical representation for anomaly detection.
AbstractList	Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost. •Unsupervised high-performance automatic COVID-19 disease detection.•Deep Denoising Convolutional Autoencoders for CT scans classification.•Distance-based hidden feature robust statistical representation for anomaly detection. Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost. Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.
ArticleNumber	116366
Author	Sarv Ahrabi, Sima Momenzadeh, Alireza Baccarelli, Enzo Scarpiniti, Michele Piazzo, Lorenzo
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Keywords	COVID-19 Pneumonia Convolutional autoencoders Computed Tomography (CT) Coronavirus disease 2019 Deep denoising autoencoder Feature learning Anomaly detection
Language	English
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Snippet	Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed...
SourceID	pubmedcentral proquest pubmed crossref elsevier
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StartPage	116366
SubjectTerms	Algorithms Anomaly detection Chest Computed tomography Computed Tomography (CT) Convolutional autoencoders Coronavirus disease 2019 Coronaviruses COVID-19 Deep denoising autoencoder Exploitation Feature learning Histograms Inspection Medical imaging Noise reduction Pneumonia Representations Viral diseases
Title	A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection
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