Abstract
Convolutional neural networks (CNN) and transfer learning are receiving a lot of attention because of the positive results achieved on image recognition and classification. Hip dysplasia is the most prevalent hereditary orthopedic disease in the dog. The definitive diagnosis is using the hip radiographic image. This article compares the results of the conventional canine hip dysplasia (CHD) classification by a radiologist using the Fédération Cynologique Internationale criteria and the computer image classification using the Inception-V3, Google’s pre-trained CNN, combined with the transfer learning technique. The experiment’s goal was to measure the accuracy of the model on classifying normal and abnormal images, using a small dataset to train the model. The results were satisfactory considering that, the developed model classified 75% of the analyzed images correctly. However, some improvements are desired and could be achieved in future works by developing a software to select areas of interest from the hip joints and evaluating each hip individually.
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References
Ginja, M.M., et al.: Early hip laxity examination in predicting moderate and severe hip dysplasia in Estrela mountain dog. J. Small Anim. Pract. 49(12), 641–646 (2008)
Loder, R.T., Todhunter, R.J.: The demographics of canine hip dysplasia in the United States and Canada. J. Vet. Med. 2017, 5723476 (2017). https://doi.org/10.1155/2017/5723476. PMID: 28386583
Kimeli, P., et al.: A retrospective study on findings of canine hip dysplasia screening in Kenya. Vet. World 8(11), 1326–1330 (2015). https://doi.org/10.14202/vetworld.2015.1326-1330
Ginja, M.M.D., Silvestre, A.M., Gonzalo-Orden, J.M., Ferreira, A.J.A.: Diagnosis, genetic control and preventive management of canine hip dysplasia: a review. Vet. J. 184(3), 269–276 (2010). https://doi.org/10.1016/j.tvjl.2009.04.009
Butler, R., Gambino, J.: Canine hip dysplasia: diagnostic imaging. Vet. Clin. North Am. Small Anim. Pract. 47(4), 777–793 (2017)
Smith, G.K., Gregor, T.P., McKelvie, P.J., O’Neill, S.M., Fordyce, H., Pressler, C.R.K.: PennHIP Training Seminar and Reference Material. Synbiotics Corporation, San Diego (2002)
Tian, Y., Jana, S., Pei, K., Ray, B.: DeepTest: automated testing of deep-neural-network-driven autonomous cars. In: IEEE/ACM 40th International Conference on Software Engineering (ICSE), pp. 303–314 (2018). https://doi.org/10.1145/3180155.3180220
Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352–2449. MIT Press Journals (2017). https://doi.org/10.1162/NECO_a_00990
Aloysius, N., Geetha, M.: A review on deep convolutional neural networks. In: International Conference on Communication and Signal Processing, pp. 588–592 (2017). https://doi.org/10.1109/ICCSP.2017.8286426
Sarkar, D., Bali, R., Ghosh, T.: Hands-On Transfer Learning with Python. Packt Publishing, Birmingham (2018)
Advanced Guide to Inception v3 on Cloud TPU Homepage. https://cloud.google.com/tpu/docs/inception-v3-advanced. Accessed 25 April 2021
ImageNet Homepage. http://www.image-net.org. Accessed 25 March 2021
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS 2012), pp. 1097–1105. Curran Associates Inc., Red Hook, NY, USA (2012)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016). http://arxiv.org/abs/1512.00567
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016). https://doi.org/10.1109/TBME.2015.2496264
TensorFlow Homepage. https://www.tensorflow.org/. Accessed 25 March 2021
Kotu, V., Deshpande, B.: Chapter 8 - Model Evaluation. Data Science (Second Edition), pp. 263–79. Morgan Kaufmann, Burlington (2019). https://doi.org/10.1016/B978-0-12-814761-0.00008-3
Freeman, E.A., Moisen, G.G.: A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Modell. 217(1–2), 48–58 (2008). https://doi.org/10.1016/j.ecolmodel.2008.05.015
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This work was supported by National Funds by FCT - Portuguese Foundation for Science and Technology, under the projects UIDB/04033/2020 and Scientific Employment Stimulus - Institutional Call - CEECINST/00127/2018 UTAD.
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Gomes, D.A., Alves-Pimenta, M.S., Ginja, M., Filipe, V. (2021). Predicting Canine Hip Dysplasia in X-Ray Images Using Deep Learning. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_29
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