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Classification of Whole Body Bone Scan Image with Bone Metastasis using CNN-based Transfer Learning

CNN 기반 전이학습을 이용한 뼈 전이가 존재하는 뼈 스캔 영상 분류

  • Yim, Ji Yeong (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Do, Thanh Cong (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Kim, Soo Hyung (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lee, Guee Sang (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lee, Min Hee (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Min, Jung Joon (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Bom, Hee Seung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Kim, Hyeon Sik (Medical Photonics Research Center, Korea Photonics Technology Institute) ;
  • Kang, Sae Ryung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Yang, Hyung Jeong (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • Received : 2022.08.12
  • Accepted : 2022.08.23
  • Published : 2022.08.31
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Abstract

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

Keywords

Acknowledgement

This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government(MSIT) (NRF-2019M3E5D1A02067961) and also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2B5B01002085).

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