Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis
Author:
Burrai Giovanni P.12ORCID, Gabrieli Andrea1ORCID, Polinas Marta1, Murgia Claudio1, Becchere Maria Paola3, Demontis Pierfranco4, Antuofermo Elisabetta12
Affiliation:
1. Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy 2. Mediterranean Center for Disease Control (MCDC), University of Sassari, Via Vienna 2, 07100 Sassari, Italy 3. Independent Researcher, 07100 Sassari, Italy 4. Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
Abstract
Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.
Funder
Università degli Studi di Sassari
Subject
General Veterinary,Animal Science and Zoology
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