Dental X-ray Image Segmentation using Octave Convolution Neural Network

Date

2020-01-01

Author

Kaya, Mete Can
Akar, Gözde

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 15B12015 dataset.

Subject Keywords

Unet, Octave Convolution, CNN, Focal Loss, Dental X-ray Image Segmentation

URI

https://hdl.handle.net/11511/96538

DOI

https://doi.org/10.1109/siu49456.2020.9302495

Conference Name

28th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

M. C. Kaya and G. Akar, “Dental X-ray Image Segmentation using Octave Convolution Neural Network,” presented at the 28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 2020, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96538.