Abstract
Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks’ performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.
Original language | English |
---|---|
Article number | 100 |
Pages (from-to) | 1-24 |
Number of pages | 24 |
Journal | Journal of Imaging |
Volume | 7 |
Issue number | 6 |
DOIs | |
Publication status | Published - 21 Jun 2021 |
Keywords
- Convolutional neural networks
- Deep learning
- Ensemble learning
- Image classification
- Medical images
- Stacking
- Transfer learning