Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study

• DOI: 10.3389/fvets.2021.640944
• Publication Date: 2021-03-26T04:52:07Z
• Authors: Massimo Salvi; Filippo Molinari; Selina Iussich; Luisa Vera Muscat...; Luca Pazzini; Silvia Benali; Barbara Banco; Francesca Abramo; Raffaella De Maria; Luca Aresu
• Keywords: Grading (engineering)

Canine cutaneous round cell tumors (RCT) represent one of the routine diagnostic challenges for veterinary pathologists. Computer-aided approaches are developed to overcome these restrictions and increase accuracy consistency diagnosis. These systems also high benefit reducing errors when a large number cases screened daily. In this study we describe ARCTA (Automated Round Cell Tumors Assessment), fully automated algorithm RCT classification mast grading in canine histopathological images. employs deep learning strategy was on 416 images 213 test set, our exhibited an excellent performance both (accuracy: 91.66%) 100%). Misdiagnoses were encountered histiocytomas train set melanomas set. For reduction grade observed but not To best knowledge, proposed model is first histological specifically medicine. Being very fast (average computational time 2.63 s), paves way effective evaluation tumors.