Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial...

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Autores principales:	Lorenzo Bonicelli, Abigail Rose Trachtman, Alfonso Rosamilia, Gaetano Liuzzo, Jasmine Hattab, Elena Mira Alcaraz, Ercole Del Negro, Stefano Vincenzi, Andrea Capobianco Dondona, Simone Calderara, Giuseppe Marruchella
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pig slaughterhouse pneumonia scoring methods artificial intelligence deep learning Veterinary medicine SF600-1100 Zoology QL1-991
Acceso en línea:	https://doaj.org/article/220957cc78744aeb8e271664e45828b6
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id	oai:doaj.org-article:220957cc78744aeb8e271664e45828b6
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spelling	oai:doaj.org-article:220957cc78744aeb8e271664e45828b62021-11-25T16:20:37ZTraining Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs10.3390/ani111132902076-2615https://doaj.org/article/220957cc78744aeb8e271664e45828b62021-11-01T00:00:00Zhttps://www.mdpi.com/2076-2615/11/11/3290https://doaj.org/toc/2076-2615The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.Lorenzo BonicelliAbigail Rose TrachtmanAlfonso RosamiliaGaetano LiuzzoJasmine HattabElena Mira AlcarazErcole Del NegroStefano VincenziAndrea Capobianco DondonaSimone CalderaraGiuseppe MarruchellaMDPI AGarticlepigslaughterhousepneumoniascoring methodsartificial intelligencedeep learningVeterinary medicineSF600-1100ZoologyQL1-991ENAnimals, Vol 11, Iss 3290, p 3290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pig slaughterhouse pneumonia scoring methods artificial intelligence deep learning Veterinary medicine SF600-1100 Zoology QL1-991
spellingShingle	pig slaughterhouse pneumonia scoring methods artificial intelligence deep learning Veterinary medicine SF600-1100 Zoology QL1-991 Lorenzo Bonicelli Abigail Rose Trachtman Alfonso Rosamilia Gaetano Liuzzo Jasmine Hattab Elena Mira Alcaraz Ercole Del Negro Stefano Vincenzi Andrea Capobianco Dondona Simone Calderara Giuseppe Marruchella Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
description	The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.
format	article
author	Lorenzo Bonicelli Abigail Rose Trachtman Alfonso Rosamilia Gaetano Liuzzo Jasmine Hattab Elena Mira Alcaraz Ercole Del Negro Stefano Vincenzi Andrea Capobianco Dondona Simone Calderara Giuseppe Marruchella
author_facet	Lorenzo Bonicelli Abigail Rose Trachtman Alfonso Rosamilia Gaetano Liuzzo Jasmine Hattab Elena Mira Alcaraz Ercole Del Negro Stefano Vincenzi Andrea Capobianco Dondona Simone Calderara Giuseppe Marruchella
author_sort	Lorenzo Bonicelli
title	Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
title_short	Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
title_full	Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
title_fullStr	Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
title_full_unstemmed	Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs
title_sort	training convolutional neural networks to score pneumonia in slaughtered pigs
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/220957cc78744aeb8e271664e45828b6
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Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs

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