The Effects of the Traditional Data Augmentation Techniques on Long Bone Fracture Detection

Abstract

Image collection and preparation phases are highly costly for machine learning algorithms. They require the majority of labeled data. Hence, the image pre-processing method, data augmentation, is commonly used. Since there are so many proposed methods for the augmentation task, this comparison study is presented to be a supporting guide for the researchers. In addition, the lack of studies with animal-based data sets makes this study more valuable. The study is investigated on a comprehensive medical image data set consists of X-ray images of many different dogs. The main goal is to determine the fracture of the long bones in dogs. Many traditional augmentation methods are employed on the data set including flipping, rotating, changing brightness and contrast of the images. Transfer learning is applied on both raw and augmented data sets as a feature extractor and Support Vector Machine (SVM) is utilized as a classifier. For the classification task, the experimental study shows that changing the contrast is the outstanding method for accuracy manner, while the rotation method has the best sensitivity value.

Keywords

• biomedical image processing
• bone fractures
• convolutional neural networks
• deep learning
• data augmentation.

References

1. Arsomngern, P., Numcharoenpinij, N., Piriyataravet, J. Teerapan, W., Hinthong W. and Phunchongharn, P. "Computer-Aided Diagnosis for Lung Lesion in Companion Animals from X-ray Images Using Deep Learning Techniques," 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka, Japan, pp. 1-6, 2019.
2. Baydan, B., Ünver, H. M.: “Dataset creation and SSD mobilenet V2 performance evaluation for dog tibia fracture detection”, II. International Ankara Congress of Scientific Research, Ankara, Turkey, 6-8 March, 2020.
3. Baydan, B., Ünver, H. M., Barışçı, N.,“Determining the Location of Tibial Fracture of Dog and Cat Using Hybridized Mask R-CNN Architecture”, Kafkas Üniversitesi Veteriner Fakültesi Dergisi, vol. 27, 3, pp. 347 – 353, 2021.
4. Calimeri, F., Marzullo, A., Stamile, C., Terracina, G. (2017). Biomedical Data Augmentation Using Generative Adversarial Neural Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science, vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_71.
5. Christian S., Vincent V., Sergey I., Jonathon S., Zbigniew W., “Rethinking the Inception Architecture for Computer Vision”, Dec. 2015.
6. Deshmukh S. and Khaparde A., "Segmentation Using CNN for BAA using Modified TW3 method," 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021, pp. 1-8, doi: 10.1109/ICECET52533.2021.9698478.
7. Ergün G. B., Güney S., Ergün T.G., Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması, Fırat Üniversitesi Fen Bilimleri Dergisi, vol. 33, pp. 125-132, 2021.
8. Ergün G. B. and Güney, S. "Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones," in IEEE Access, vol. 9, pp. 109004-109011, 2021, doi: 10.1109/ACCESS.2021.3101040.

9. Frid-Adar, M., Klang, E., Amitai, M., Goldberger J., and Greenspan, H. "Synthetic data augmentation using GAN for improved liver lesion classification," 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 289-293, doi: 10.1109/ISBI.2018.8363576
10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., (2014). Generative Adversarial Nets (PDF). Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680.
11. Guo K. et al., "Angel-Eye: A Complete Design Flow for Mapping CNN onto Customized Hardware," 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Pittsburgh, PA, 2016, pp. 24-29.
12. Hernández-García A., König P. “Further Advantages of Data Augmentation on Convolutional Neural Networks,” Artificial Neural Networks and Machine Learning ICANN 2018. Lecture Notes in Computer Science, vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_10.
13. Hussain Z., Gimenez F., Yi D., Rubin D., “Differential Data Augmentation Techniques for Medical Imaging Classification Tasks,” AMIA Annu Symp Proc. pp. 979-984, 2018.
14. Jangid R. G, Naik K., N. and Francis R. M., "Detection of Abnormality in Human Hard Tissue using Edge Detection Operators," 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), 2022, pp. 1-5, doi: 10.1109/ICACTA54488.2022.9753141.
15. Jia S, Wang P, Jia P, Hu S. “Research on data augmentation for image classification based on convolutional neural networks” Chinese automation congress, 2017. p. 4165–70.
16. LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; Jackel, L. D. (December 1989). "Backpropagation Applied to Handwritten Zip Code Recognition". Neural Computation. 1 (4): 541–551.
17. Lewis, G., (2019). Musculoskeletal Development of the Puppy: Birth to Twelve Months. Winter. 41-44.
18. Li Y., "Research and Application of Deep Learning in Image Recognition," 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 2022, pp. 994-999, doi: 10.1109/ICPECA53709.2022.9718847.
19. McEvoy, F. J., Amigo, J. M. “Using machine learning to classify image features from canine pelvic radiographs: evaluation of partial least squares discriminant analysis and artificial neural network models,” Veterinary Radiology & Ultrasound, vol. 54 (2), pp. 122-126, 2013.
20. Pharmacology, Toxicology & Therapeutics, “The Skeleton, Cheapter 4”, https://veteriankey.com/the-skeleton/ access: 21.04.2022, 15.42.
21. Perez, L., and Wang. J., “The Effectiveness of Data Augmentation in Image Classification using Deep Learning.” ArXiv abs/1712.04621 (2017).
22. Sajjad M. et al. “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” Journal OF Computational Science, vol. 30, pp.174-182, 2019.
23. Shijie, J., Ping W., Peiyi J. and Siping, H., "Research on data augmentation for image classification based on convolution neural networks," Chinese Automation Congress (CAC), pp. 4165-4170, 2017.
24. Shin HC. et al. “Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks,” Simulation and Synthesis in Medical Imaging. SASHIMI 2018. Lecture Notes in Computer Science, vol 11037. Springer, Cham. https://doi.org/10.1007/978-3-030-00536-8_1
25. Shorten, C., Khoshgoftaar, T.M. A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60 (2019). https://doi.org/10.1186/s40537-019-0197-0
26. Shunjiro N., Mizuho N., Masahiro Y., Keita N., Kaori T., “Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques,” Computers in Biology and Medicine, vol. 121, 2020.
27. Tabarestani S. S., Aghagolzadeh A. and Ezoji M., "Bone Fracture Detection and Localization on MURA Database Using Faster-RCNN," 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS), 2021, pp. 1-6, doi: 10.1109/ICSPIS54653.2021.9729393.