Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study - Hubbard-Perez, Magdalena; Luchian, Andreea; Milford, Charles; Ressel, Lorenzo (Frontiers in Veterinary Science, 2024)
Artificial intelligence has seen significant growth in its ability to classify different types of tumors in humans, thanks to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on application of AI in domestic species are scarce. This research aims to employ Deep Learning (DL) through Convolutional Neural Networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, a total of 1530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (n=4), and three different lymphoma subtypes: Diffuse large B cell lymphoma (DLBCL; n=5), Lymphoblastic (LBL; n=5), and Marginal Zone Lymphoma (MZL; n=3). The CNN was able to correctly identify 456 images of the possible 457 of the Test set, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and to validate the network's ability to classify a broader range of lymphomas.