Abstract
The manual health inspection are becoming the bottleneck at poultry processing plants. We present a computer vision method for automatic diagnosis of broiler livers. The non-rigid livers, of varying shape and sizes, are classified in patches by a convolutional neural network, outputting maps with probabilities of the three most common diseases. A Random Forest classifier combines the maps to a single diagnosis. The method classifies 77.6% livers correctly in a problem that is far from trivial.
Cite
CITATION STYLE
Jørgensen, A., Fagertun, J., & Moeslund, T. B. (2017). Diagnosis of broiler livers by classifying image patches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10269 LNCS, pp. 374–385). Springer Verlag. https://doi.org/10.1007/978-3-319-59126-1_31
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