Yazar "Baydan, Berker" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    Detection of tibial fractures in cats and dogs with deep learning
    (Ankara Univ Press, 2021) Baydan, Berker; Unver, Halil Murat
    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.
  • [ X ]
    Öğe
    Determining the Location of Tibial Fracture of Dog and Cat Using Hybridized Mask R-CNN Architecture
    (Kafkas Univ, Veteriner Fakultesi Dergisi, 2021) Baydan, Berker; Barisci, Necaatti N.; Unver, Halil Murat
    The aim of this study is to hybridize the original backbone structure used in the Mask R-CNN framework, and to detect fracture location in dog and cat tibia fractures faster and with higher performance. With the hybrid study, it will be ensured that veterinarians help diagnose fractures on the tibia with higher accuracy by using a computerized system. In this study, a total of 518 dog and cat fracture tibia images that obtained from universities and institutions were used. F1 score value of this study on total dataset was found to be 85.8%. F1 score value of this study on dog dataset was found to be 87.8%. F1 score value of this study on cat dataset was found to be 77.7%. With the developed hybrid system, it was determined that the localization of the fracture in an average tibia image took 2.88 seconds. The results of the study showed that the hybrid system developed would be beneficial in terms of protecting animal health by making more successful and faster detections than the original Mask R-CNN architecture.