Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Computer Aided Diagnosis Algorithm for Classification of Malignant Melanoma based on Deep Learning

딥 러닝 기반의 악성흑색종 분류를 위한 컴퓨터 보조진단 알고리즘

  • 임상헌 (계명대학교 의용공학과) ;
  • 이명숙 (계명대학교 타불라라사칼리지)
  • Received : 2018.09.20
  • Accepted : 2018.11.30
  • Published : 2018.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

Keywords

References

  1. 송계용.정경천.조광현.지제근.함의근, "악성 흑색종의 임상 및 병리조직학적 고찰," 대한병리학회지, 제124권, 제6호, 1997, pp. 566-573.
  2. ISIC Archive, https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main, 2018년 5월 검색.
  3. Jerant, A. F., Johnson, J. T., Sheridan, C. D., Caffrey, T. J., "Early detection and treatment of skin cancer," Am Fam Physician, 62(2), 2000, pp. 357-368.
  4. Yuexuang, L., Linlin, S., "Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network," Sensors, Vol.18, No.2, 2017, pp. 1-4. https://doi.org/10.3390/s18010001
  5. Wiebke, S., Lisa, Z., Dik, S., et al., "Initial misdiagnosis of melanoma located on the foot is associated with poorer prognosis, Medicine," Vol.95, No.29, 2016, pp. 4332. https://doi.org/10.1097/MD.0000000000004332
  6. Krige, J. E. J., et al. "Delay in the diagnosis of cutaneous malignant melanoma, A prospective study in 250 patients," Cancer, vol.68, No.9, 1991, pp. 2064-2072. https://doi.org/10.1002/1097-0142(19911101)68:9<2064::AID-CNCR2820680937>3.0.CO;2-3
  7. Metzger, S., et al. "Extent and consequences of physician delay in the diagnosis of acral melanoma," Melanoma research, Vol.8, No.2, 1998, pp. 181-186. https://doi.org/10.1097/00008390-199804000-00014
  8. Grin, C. M., Kent P. F., and Jane M. G., Dermoscopy: a review, Dermatologic clinics, 2002, pp. 641-646.
  9. 조영석, "IOT를 이용한 DB기반 농작물 자동재배에 관한 연구," (사)디지털산업정보학회 논문지, 제13권, 4호, 2017, pp. 25-31.
  10. 문원식, "실시간 분산 시스템에서 heartbeat 시그널을 이용한 장애 검출," (사)디지털산업정보학회논문지, 제14권, 2호, 2018, pp. 39-44.
  11. Rigel, Darrell S., et al. ABCDE-an evolving concept in the early detection of melanoma, Archives of dermatology, Vol.141, No.8, 2016, pp. 1032-1036.
  12. Yann, L., Yoshua, B., and Geoffrey, H., Deep learning, nature 521, 2015, pp. 436-444. https://doi.org/10.1038/nature14539
  13. Kevin, P. M., Machine Learning: A Probabilistic Perspective, MIT, 2012.
  14. Alex, K., Ilya, S., and Geoffrey, E. H., "Imagenet classification with deep convolutional neural networks." ACM, Vol.60, No.6, 2017, pp. 84-90. https://doi.org/10.1145/3065386
  15. Olaf, R., Philipp, F., Thomas, B., "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234-241.
  16. Yeghiazaryan, V., and Irina V., An overview of current evaluation methods used in medical image segmentation, Tech. Rep. CS-RR-15-08, Department of Computer Science, University of Oxford, Oxford, UK, 2015.
  17. Kaiming, H., et al, Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  18. Kaiming, H., et al., Identity mappings in deep residual networks, European conference on computer vision, Springer, Cham, 2016.

Cited by

  1. 결합 신경망을 이용한 여권 MRZ 정보 인식 vol.15, pp.4, 2018, https://doi.org/10.17662/ksdim.2019.15.4.149
  2. CNN 기반의 IEEE 802.11 WLAN 프레임 포맷 검출 vol.16, pp.2, 2018, https://doi.org/10.17662/ksdim.2020.16.2.027
  3. IEEE 802.11ac 변조 방식의 딥러닝 기반 분류 vol.16, pp.2, 2020, https://doi.org/10.17662/ksdim.2020.16.2.045
  4. 심장비대증 환자의 흉부 X선 영상에 대한 Inception V3 알고리즘의 분류 성능평가 vol.15, pp.4, 2018, https://doi.org/10.7742/jksr.2021.15.4.455