지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.15 No.3
- 발행연도
- 2021.9
- 수록면
- 115 - 124 (10page)
- DOI
- 10.5626/JCSE.2021.15.3.115
이용수
초록· 키워드
오류제보하기
Among the different types of skin cancer, melanoma is the one with the highest death rate. Therefore, the early detection of melanoma and the development of technologies that can assist in this task have become significantly important. Convolutional neural networks are one of the most popular skin cancer classification methods. However, most of the available skin cancer datasets include images with lesions that are hard to differentiate from healthy skin or with a high presence of hair that can occlude the lesion. This characteristics of the images makes it harder to extract lesion features. Therefore, utilizing segmentation to extract the lesion location is an important step to reduce hair noise and improve lesion analysis. In this paper, two combining methods for segmentation and classification were explored: concatenation and multiplication. By utilizing these methods, it was possible to improve the accuracy of different neural network architectures by around 1% when compared to unmodified models without segmentation. The best-performing model was selected for further training. This model in conjunction with the segmentation module allowed for the correct re-classification of around 10% of the total examples in the dataset, indicating that a segmentation phase leads to an overall accuracy improvement and suggested that by improving the segmentation, an improvement on the overall accuracy can be obtained.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORK
Ⅲ. METHODOLOGY
Ⅳ. EXPERIMENTAL RESULTS AND DISCUSSION
Ⅴ. CONCLUSION
REFERENCES
참고문헌 (28)
참고문헌 신청
American Cancer Society, “Cancer Fact & Figures 2020,” 2020 [Online]. Available: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-factsfigures-2020.html.
J. W. Chang, J. Guo, C. Y. Hung, S. Lu, S. J. Shin, R. Quek, et al., “Sunrise in melanoma management: time to focus on melanoma burden in Asia,” Asia-Pacific Journal of Clinical Oncology, vol. 13, no. 6, pp. 423-427, 2017.
Y. J. Park, G. H. Kwon, J. O. Kim, N. K. Kim, W. S. Ryu, and K. S. Lee, “A retrospective study of changes in skin cancer characteristics over 11 years,” Archives of Craniofacial Surgery, vol. 21, no. 2, pp. 87-91, 2020.
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115-118, 2017.
J. Koehoorn, A. C. Sobiecki, D. Boda, A. Diaconeasa, S. Doshi, S. Paisey, A. Jalba, and A. Telea, “Automated digital hair removal by threshold decomposition and morphological analysis,” in Mathematical Morphology and Its Applications to Signal and Image Processing. Cham, Switzerland: Springer, 2015, pp. 15-26.
J. W. Chang, J. Guo, C. Y. Hung, S. Lu, S. J. Shin, R. Quek, et al., “Sunrise in melanoma management: time to focus on melanoma burden in Asia,” Asia-Pacific Journal of Clinical Oncology, vol. 13, no. 6, pp. 423-427, 2017.
Y. J. Park, G. H. Kwon, J. O. Kim, N. K. Kim, W. S. Ryu, and K. S. Lee, “A retrospective study of changes in skin cancer characteristics over 11 years,” Archives of Craniofacial Surgery, vol. 21, no. 2, pp. 87-91, 2020.
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115-118, 2017.
J. Koehoorn, A. C. Sobiecki, D. Boda, A. Diaconeasa, S. Doshi, S. Paisey, A. Jalba, and A. Telea, “Automated digital hair removal by threshold decomposition and morphological analysis,” in Mathematical Morphology and Its Applications to Signal and Image Processing. Cham, Switzerland: Springer, 2015, pp. 15-26.
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