Araştırma Makalesi
Yıl 2021,
, 283 - 290, 30.06.2021
Öz
Kaynakça
- Bennour EM, Abushhiwa MA, Ben Ali L, et al (2014): A retrospective study on appendicular fractures in dogs and cats in Tripoli – Libya. J Vet Adv, 4, 425-431.
- Chung SW, Han SS, Lee JW, et al (2018): Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop, 89, 468-473.
- Eksi Z, Dandil E, Cakiroglu M (2012): Computer aided bone fracture detection. In: 2012 20th Signal Processing and Communications Applications Conference (SIU). Muğla, Turkey.
- Glyde M, Arnett R (2006): Tibial fractures in the dog and cat: options for management. Ir Vet J, 59, 290-295.
- Hayashi K, Kapatkin AS (2012): Fractures of the tibia and fibula, 999-1014. In: KM Tobias, SA Johnston (Eds), Veterinary Surgery Small Animal. Volume One, E-BOOK: 2-Volume Set, Elsevier Inc., Canada.
- He K, Hkioxari G, Dollar P et al (2018): Mask R-CNN. 2980-2988. In: IEEE International Conference on Computer Vision (ICCV). Venice, Italy. arXiv:1703.06870v3 [cs.CV].
- Joshi D, Singh T (2020): A survey of fracture detection techniques in bone x ray images. Artif Intell Rev, 53, 4475–4517.
- Kalmet PHS, Sanduleanu S, Primakov S et al (2020): Deep learning in fracture detection: a narrative review. Acta Orthop, 91, 215-220.
- Khan M, Sirdeshmukh, SPSMA, Javed K (2016): Evaluation of Bone Fracture in Animal Model Using Bio‐electrical Impedance Analysis. Perspectives in Science, 8, 567-569.
- Khatik I (2017): A study of various bone fracture detection techniques. Int J Eng Comput Sci, 6, 21418-21423.
- Kim DH, MacKinnon T (2017): Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol, 73, 439-445.
- Liu W, Anguelov D, Erhan D et al (2016): SSD: Single Shot MultiBox Detector. arXiv:1512.02325 [cs.CV]. Available at https://arxiv.org/pdf/1512.02325.pdf. (Accessed October 21, 2020).
- Mahendran SK, Santhosh Baboo S (2011): An enhanced tibia fracture detection tool using image processing and classification fusion techniques in x-ray images. GJCST, 11, 23-28.
- Olczak J, Fahlberg N, Maki A et al (2017). Artificial intelligence for analysing orthopaedic trauma radiographs. Acta Orthop, 88, 581-586.
- Rani S, Kumari M, Amulya G et al (2019): Leg bone fracture segmentation and detection using advanced morphplogical techniques. Int J Recent Technol Eng, 8, 1246-1249.
- Ravi D, Wong C, Deligianni F et al (2017): Deep Learning for Health Informatics. IEEE J Biomed Health, 21, 4-21.
- Tsuruoka Y, Tsujii J (2003): Boosting precision and recall of dictionary-based protein name recognition. 41-48. In: Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine. Sapporo, Japan.
- Tychsen-Smith L, Petersson L (2018): Improving object localization with fitness nms and bounded IOU loss. arXiv:1711.00164v3 [cs.CV]. Available at https://arxiv.org/pdf/1711.00164.pdf. (Accessed July 21, 2020).
- Tzutalin (2015): LabelImg. Git code. Available at https://github.com/tzutalin/labelImg. (Accessed February 27, 2020).
Yıl 2021,
, 283 - 290, 30.06.2021
Öz
The aim of this study is to classify tibia (fracture/no fracture) on whole/partial body digital images of cats and dogs, and to localize the fracture on fracture tibia by using deep learning methods. This study provides to diagnose fracture on tibia more accurately, quickly and safe for clinicians. In this study, a total of 1488 dog and cat images that were obtained from universities and institutions were used. Three different studies were implemented to detect fracture tibia. In the first phase of the first study, tibia was classified automatically as fracture or no fracture with Mask R-CNN. In the second phase, the fracture location in the fracture tibia image that obtained from the first phase was localized with Mask R-CNN. In the second study, the fracture location was directly localized with Mask R-CNN. In the third study, fracture location in the fracture tibia that obtained from the first phase of first study was localized with SSD. The accuracy and F1 score values in first phase of first study were 74% and 85%, respectively and F1 score value in second phase of first study was 84.5%. The accuracy and F1 score of second study were 52.1% and 68.5%, respectively. The F1 score of third study was 46.2%. The results of the research showed that the first study was promising for detection of fractures in the tibia and the dissemination of the fracture diagnosis with the help of such smart systems would also be beneficial for animal welfare.
Anahtar Kelimeler
Kaynakça
- Bennour EM, Abushhiwa MA, Ben Ali L, et al (2014): A retrospective study on appendicular fractures in dogs and cats in Tripoli – Libya. J Vet Adv, 4, 425-431.
- Chung SW, Han SS, Lee JW, et al (2018): Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop, 89, 468-473.
- Eksi Z, Dandil E, Cakiroglu M (2012): Computer aided bone fracture detection. In: 2012 20th Signal Processing and Communications Applications Conference (SIU). Muğla, Turkey.
- Glyde M, Arnett R (2006): Tibial fractures in the dog and cat: options for management. Ir Vet J, 59, 290-295.
- Hayashi K, Kapatkin AS (2012): Fractures of the tibia and fibula, 999-1014. In: KM Tobias, SA Johnston (Eds), Veterinary Surgery Small Animal. Volume One, E-BOOK: 2-Volume Set, Elsevier Inc., Canada.
- He K, Hkioxari G, Dollar P et al (2018): Mask R-CNN. 2980-2988. In: IEEE International Conference on Computer Vision (ICCV). Venice, Italy. arXiv:1703.06870v3 [cs.CV].
- Joshi D, Singh T (2020): A survey of fracture detection techniques in bone x ray images. Artif Intell Rev, 53, 4475–4517.
- Kalmet PHS, Sanduleanu S, Primakov S et al (2020): Deep learning in fracture detection: a narrative review. Acta Orthop, 91, 215-220.
- Khan M, Sirdeshmukh, SPSMA, Javed K (2016): Evaluation of Bone Fracture in Animal Model Using Bio‐electrical Impedance Analysis. Perspectives in Science, 8, 567-569.
- Khatik I (2017): A study of various bone fracture detection techniques. Int J Eng Comput Sci, 6, 21418-21423.
- Kim DH, MacKinnon T (2017): Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol, 73, 439-445.
- Liu W, Anguelov D, Erhan D et al (2016): SSD: Single Shot MultiBox Detector. arXiv:1512.02325 [cs.CV]. Available at https://arxiv.org/pdf/1512.02325.pdf. (Accessed October 21, 2020).
- Mahendran SK, Santhosh Baboo S (2011): An enhanced tibia fracture detection tool using image processing and classification fusion techniques in x-ray images. GJCST, 11, 23-28.
- Olczak J, Fahlberg N, Maki A et al (2017). Artificial intelligence for analysing orthopaedic trauma radiographs. Acta Orthop, 88, 581-586.
- Rani S, Kumari M, Amulya G et al (2019): Leg bone fracture segmentation and detection using advanced morphplogical techniques. Int J Recent Technol Eng, 8, 1246-1249.
- Ravi D, Wong C, Deligianni F et al (2017): Deep Learning for Health Informatics. IEEE J Biomed Health, 21, 4-21.
- Tsuruoka Y, Tsujii J (2003): Boosting precision and recall of dictionary-based protein name recognition. 41-48. In: Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine. Sapporo, Japan.
- Tychsen-Smith L, Petersson L (2018): Improving object localization with fitness nms and bounded IOU loss. arXiv:1711.00164v3 [cs.CV]. Available at https://arxiv.org/pdf/1711.00164.pdf. (Accessed July 21, 2020).
- Tzutalin (2015): LabelImg. Git code. Available at https://github.com/tzutalin/labelImg. (Accessed February 27, 2020).
Toplam 19 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Veteriner Cerrahi |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2021 |
| Yayımlandığı Sayı | Yıl 2021 |