Dental X-ray Image Segmentation using Octave Convolution Neural Network

Kaya, Mete Can
Akar, Gözde
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.
28th Signal Processing and Communications Applications Conference (SIU)


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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: