A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL
Tekijät: Yadav Dhirendra Prasad, Sharma Ashish, Athithan Senthil, Bhola Abhishek, Sharma Bhisham, Ben Dhaou Imed
Kustantaja: MDPI
Julkaisuvuosi: 2022
Journal: Sensors
Tietokannassa oleva lehden nimi: SENSORS
Lehden akronyymi: SENSORS-BASEL
Artikkelin numero: 5823
Vuosikerta: 22
Numero: 15
Sivujen määrä: 23
DOI: https://doi.org/10.3390/s22155823
Verkko-osoite: https://www.mdpi.com/1424-8220/22/15/5823
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/176199530
An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
