Abstract

Localization of human long bones in ultrasound images has quite complex challenges. This is due to a representation of the reflection of a sound wave emitted by a B-scan sensor. The ultrasound scan does not only display bone specimens, but also contains muscles, soft tissue, and other parts under the skin tissue Therefore we need a system that can automatically recognize bone specimens in ultrasound images. This study implements deep learning based learning systems using the convolutional neural network (CNN) method with YOLOv3. The training results from the network detector with IoU threshold 0.5 can recognize bone objects in mAP@50, mAP@75 and mAP@50:95 with values of 99.98, 97.68 and 85.67 respectively. And for the results of training the network detector with IoU threshold 0.75 can recognize bone objects in mAP@50, mAP@75 and mAP@50:95 with values of 99.96, 97.46 and 86.35 respectively.

Original languageEnglish
Title of host publication2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129655
DOIs
Publication statusPublished - Nov 2019
Event2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Surabaya, Indonesia
Duration: 19 Nov 201920 Nov 2019

Publication series

Name2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
Volume2019-November

Conference

Conference2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019
Country/TerritoryIndonesia
CitySurabaya
Period19/11/1920/11/19

Keywords

  • CNN
  • YOLOv3
  • bone USG

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