Dental X-ray Image Segmentation using Octave Convolution Neural Network
| Autor: | Gozde Bozdagi Akar, Mete Can Kaya |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science Image segmentation Convolutional neural network Object detection 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences 0302 clinical medicine X ray image Octave Computer vision Segmentation Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | SIU |
| DOI: | 10.1109/siu49456.2020.9302495 |
| Popis: | In this paper, we present a Unet architecture made of octave convolution for dental image segmentation problem. In this architecture, the requirements for memory and accuracy are significantly improved compared to previous works in the literature. Compare to state-of-art models on this topic the classification accuracy in dental image segmentation is increased by %2, and the memory usage is decreased by %70. Suggested architecture showed a performance of success on ISBI2015 dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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