Automated Classification for Visual-Only Post-Mortem Inspection of Porcine Pathology

Stephen McKenna (Lead / Corresponding author), Telmo Amaral, Ilias Kyriazakis

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Several advantages would arise from the automated detection of pathologies of pig carcasses, including avoidance of the inherent risks of subjectivity and variability between human observers. Here, we develop a novel automated classification of two porcine offal pathologies at abattoir: a focal, localized pathology of the liver and a diffuse pathology of the heart, as cases in point. We develop a pattern recognition system based on machine learning to identify those organs that exhibit signs of the pathology of interest. Specifically, deep neural networks are trained to produce probability heat maps highlighting regions on the surface of an organ potentially affected by a given condition. A final classification stage then decides whether a given organ is affected by the condition in question based on statistics computed from the heat map. We compare outcomes of automated classification with classification by expert pathologists. Results show classification of liver and heart pathologies in agreement with an expert at levels comparable to, or exceeding, inter-expert agreement. A system using methods such as those presented here has potential to overcome the limitations of human-based abattoir inspection, especially if this is based on visual-only inspection, and ultimately to provide a new gold standard for pathology.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Early online date20 Jan 2020
Publication statusE-pub ahead of print - 20 Jan 2020


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