A custom deep learning architecture with image augmentation for intelligent gastrointestinal tract tissue classification - Wieczorek, Michał; Wojtas, Natalia; Wituła, Roman; Krawczyk, Aleksandra; Rycerz, Karol (Zielona Góra: Uniwersytet Zielonogórski)
The origin of sampled tissue and characteristics of healthy organs is important for potential abnormality detection in veterinary medicine. Most often such information is given during the sampling process, but in some cases there is a possibility of mislabeling, especially in the education sector where some microscopic preparations might be made by students without proper knowledge or the inspected tissues are not of fully known origin. Occasionally, it is possible to determine the affiliation by searching characteristics of the organ in a sample; however, this is not always possible even for a skilled professional as some tissues vary too little between their counterparts in different species or even in different organs of the same species.
Keywords: deep learning; artificial intelligence; image augmentation; tissue classification; veterinary medicine