Forbes, “Pet Ownership Statistics 2024,” https://www.forbes.com/advisor/pet-insurance/pet-ownership-statistics

KB Management Research Institute, “2023 Korea Pet Report,” https://www.kbfg.com/kbresearch/report/reportView.do?reportId=2000396

D. G. O'Neill, D. B. Church, P. D. McGreevy, P. C. Thomson, and D. C. Brodbelt, “Prevalence of disorders recorded in dogs attending primary-care veterinary practices in England,” PloS One, vol. 9, no. 3, article no. e90501, 2014. DOI:10.1371/journal.pone.0090501

A. Smith, P. W. Carroll, S. Aravamuthan, E. Walleser, H. Lin, K. Anklam, ... and N. Apostolopoulos, “Computer vision model for the detection of canine pododermatitis and neoplasia of the paw,” Veterinary Dermatology, vol. 35, no. 2, pp. 138-147, 2024. DOI:10.1111/vde.13221

S. Hwang, H. K. Shin, J. M. Park, B. Kwon, and M. G. Kang, “Classification of dog skin diseases using deep learning with images captured from multispectral imaging device,” Molecular & Cellular Toxicology, vol. 18, no. 3, pp. 299-309, 2022. DOI:10.1007/s13273-022-00249-7

M. Salvi, F. Molinari, S. Iussich, L. V. Muscatello, L. Pazzini, S. Benali, ... and L. Aresu, “Histopathological classification of canine cutaneous round cell tumors using deep learning: a multi-center study,” Frontiers in Veterinary Science, vol. 8, article no. 640944, 2021. DOI:10.3389/fvets.2021.640944

G. Dank, T. Buber, G. Polliack, G. Aviram, A. Rice, A. Yehudayoff, and M. S. Kent, “A pilot study for a non-invasive system for detection of malignancy in canine subcutaneous and cutaneous masses using machine learning,” Frontiers in Veterinary Science, vol. 10, article no. 1109188, 2023. DOI:10.3389/fvets.2023.1109188

E. Goceri, “Diagnosis of skin diseases in the era of deep learning and mobile technology,” Computers in Biology and Medicine, vol. 134, no. 104458, 2021. DOI:10.1016/j.compbiomed.2021.104458

AI HUB, “Pet skin disease data,” https://www.aihub.or.kr/aihubdata/data

E. D. Cubuk, B. Zoph, J. Shlens and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702-703, 2020.

P. Chen, S. Liu, H. Zhao, and J. Jia, “Gridmask data augmentation,” arXiv preprint arXiv:2001.04086, 2020. DOI:10.48550/arXiv.2001.04086

J. H. Lee, J. W. Chae, and H. C. Cho, “Improved classification of different brain tumors in MRI scans using Patterned-GridMask,” IEEE Access, 2024. DOI:10.1109/ACCESS.2024.3377105

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, ... and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.

I. Loshchilov, and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017. DOI:10.48550/arXiv.1711.05101

D. P. Kingma, and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. DOI:10.48550/arXiv.1412.6980