Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

SIMPLE SUMMARY: Digital pathology (DP) and computer-aided diagnosis (CAD) are rapidly evolving fields that have great potential for improving the accuracy and efficiency of cancer diagnosis, including that of canine mammary tumors (CMTs), the most common neoplasm in female dogs. The work presents a study on the development of CAD systems for the automated classification of CMTs utilizing convolutional neural networks (CNNs) to extract features from histopathological images of CMTs and classify them into benign or malignant tumors. The study shows that the proposed framework can accurately distinguish between benign and malignant CMTs, with testing accuracies ranging from 0.63 to 0.85. The study emphasizes how digital pathology and CAD could help veterinarians and pathologists in accurately diagnosing the tumor type, which is crucial in determining the optimal course of treatment. Overall, digital pathology and CAD are promising tools that could improve the accuracy and efficiency of cancer diagnosis, including that of canine mammary tumors. 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.