Abstract
To date, the number of people who have companion animals has gradually increased and the need for advancement in veterinary care and pet health care has been increased. Deep learning models are taking their places in healthcare and can be used for detecting diseases. We aimed to build and validate a framework for auxiliary diagnosis of pet diseases in everyday life before hospital visits. Our framework utilizes disease image classification and natural language models with Swin-Transformer and Bidirectional Encoder Representations from Transformers as the backbone, respectively, and both presented the accuracy of 84.5% and 84%, respectively. This proposed framework can be useful in understanding animals’ symptoms for pet owners as well as assisting a veterinarian for diagnosis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
