Dental X-ray Image Segmentation using Octave Convolution Neural Network

被引：2
|
作者：
Kaya, Mete Can [1 ]
Akar, Gozde Bozdagi [1 ]
机构：
[1] Orta Dogu Tekn Univ, Elekt & Elekt Muhendisligi, Ankara, Turkey
来源：
2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2020年
关键词：
Unet; Octave Convolution; CNN; Focal Loss; Dental X-ray Image Segmentation;
D O I：
10.1109/siu49456.2020.9302495
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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页数：4
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