Automatic classification of canine thoracic radiographs using deep learning

Abstract The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particula...

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Autores principales:	Tommaso Banzato, Marek Wodzinski, Silvia Burti, Valentina Longhin Osti, Valentina Rossoni, Manfredo Atzori, Alessandro Zotti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da5a472551f64eec84e60932f09ad09d
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spelling	oai:doaj.org-article:da5a472551f64eec84e60932f09ad09d2021-12-02T12:11:50ZAutomatic classification of canine thoracic radiographs using deep learning10.1038/s41598-021-83515-32045-2322https://doaj.org/article/da5a472551f64eec84e60932f09ad09d2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83515-3https://doaj.org/toc/2045-2322Abstract The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particularly in the context of veterinary medicine. In this study, a novel method, based on multi-label deep convolutional neural network (CNN), for the classification of thoracic radiographs in dogs was developed. All the thoracic radiographs of dogs performed between 2010 and 2020 in the institution were retrospectively collected. Radiographs were taken with two different radiograph acquisition systems and were divided into two data sets accordingly. One data set (Data Set 1) was used for training and testing and another data set (Data Set 2) was used to test the generalization ability of the CNNs. Radiographic findings used as non mutually exclusive labels to train the CNNs were: unremarkable, cardiomegaly, alveolar pattern, bronchial pattern, interstitial pattern, mass, pleural effusion, pneumothorax, and megaesophagus. Two different CNNs, based on ResNet-50 and DenseNet-121 architectures respectively, were developed and tested. The CNN based on ResNet-50 had an Area Under the Receive-Operator Curve (AUC) above 0.8 for all the included radiographic findings except for bronchial and interstitial patterns both on Data Set 1 and Data Set 2. The CNN based on DenseNet-121 had a lower overall performance. Statistically significant differences in the generalization ability between the two CNNs were evident, with the CNN based on ResNet-50 showing better performance for alveolar pattern, interstitial pattern, megaesophagus, and pneumothorax.Tommaso BanzatoMarek WodzinskiSilvia BurtiValentina Longhin OstiValentina RossoniManfredo AtzoriAlessandro ZottiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tommaso Banzato Marek Wodzinski Silvia Burti Valentina Longhin Osti Valentina Rossoni Manfredo Atzori Alessandro Zotti Automatic classification of canine thoracic radiographs using deep learning
description	Abstract The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particularly in the context of veterinary medicine. In this study, a novel method, based on multi-label deep convolutional neural network (CNN), for the classification of thoracic radiographs in dogs was developed. All the thoracic radiographs of dogs performed between 2010 and 2020 in the institution were retrospectively collected. Radiographs were taken with two different radiograph acquisition systems and were divided into two data sets accordingly. One data set (Data Set 1) was used for training and testing and another data set (Data Set 2) was used to test the generalization ability of the CNNs. Radiographic findings used as non mutually exclusive labels to train the CNNs were: unremarkable, cardiomegaly, alveolar pattern, bronchial pattern, interstitial pattern, mass, pleural effusion, pneumothorax, and megaesophagus. Two different CNNs, based on ResNet-50 and DenseNet-121 architectures respectively, were developed and tested. The CNN based on ResNet-50 had an Area Under the Receive-Operator Curve (AUC) above 0.8 for all the included radiographic findings except for bronchial and interstitial patterns both on Data Set 1 and Data Set 2. The CNN based on DenseNet-121 had a lower overall performance. Statistically significant differences in the generalization ability between the two CNNs were evident, with the CNN based on ResNet-50 showing better performance for alveolar pattern, interstitial pattern, megaesophagus, and pneumothorax.
format	article
author	Tommaso Banzato Marek Wodzinski Silvia Burti Valentina Longhin Osti Valentina Rossoni Manfredo Atzori Alessandro Zotti
author_facet	Tommaso Banzato Marek Wodzinski Silvia Burti Valentina Longhin Osti Valentina Rossoni Manfredo Atzori Alessandro Zotti
author_sort	Tommaso Banzato
title	Automatic classification of canine thoracic radiographs using deep learning
title_short	Automatic classification of canine thoracic radiographs using deep learning
title_full	Automatic classification of canine thoracic radiographs using deep learning
title_fullStr	Automatic classification of canine thoracic radiographs using deep learning
title_full_unstemmed	Automatic classification of canine thoracic radiographs using deep learning
title_sort	automatic classification of canine thoracic radiographs using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da5a472551f64eec84e60932f09ad09d
work_keys_str_mv	AT tommasobanzato automaticclassificationofcaninethoracicradiographsusingdeeplearning AT marekwodzinski automaticclassificationofcaninethoracicradiographsusingdeeplearning AT silviaburti automaticclassificationofcaninethoracicradiographsusingdeeplearning AT valentinalonghinosti automaticclassificationofcaninethoracicradiographsusingdeeplearning AT valentinarossoni automaticclassificationofcaninethoracicradiographsusingdeeplearning AT manfredoatzori automaticclassificationofcaninethoracicradiographsusingdeeplearning AT alessandrozotti automaticclassificationofcaninethoracicradiographsusingdeeplearning
_version_	1718394603965513728

Automatic classification of canine thoracic radiographs using deep learning

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