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 ORCID: 0000-0002-0925-3923 and Ressel, Lorenzo ORCID: 0000-0002-6614-1223 (2024) Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study. Frontiers in Veterinary Science, 10. 1309877-.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.3389/fvets.2023.1309877

Abstract

<jats:p>Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the 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, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (<jats:italic>n</jats:italic> = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; <jats:italic>n</jats:italic> = 5), lymphoblastic (LBL; <jats:italic>n</jats:italic> = 5), and marginal zone lymphoma (MZL; <jats:italic>n</jats:italic> = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, 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 validate the ability of the network to classify a broader range of lymphomas.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, canine, convolutional neural network, lymphoma, transfer learning
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jan 2024 08:54
Last Modified:	31 Jan 2024 15:32
DOI:	10.3389/fvets.2023.1309877
Open Access URL:	https://doi.org/10.3389/fvets.2023.1309877
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177996