Dermoscopic Image Analysis for Skin Lesion Classification Using Dense Convolutional Network-Reference-Cited by-同舟云学术

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Dermoscopic Image Analysis for Skin Lesion Classification Using Dense Convolutional Network

Published:2022-09-27 Issue: Volume: Page:573-583
ISSN:2524-7565
Container-title:Algorithms for Intelligent Systems
language:
Short-container-title:

Author:

Purwar Prasoon^ORCID,Bhardwaj Neha

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-3951-8_44

Reference24 articles.

1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. Cancer J Clin 68(1):7–30

2. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:180161. https://doi.org/10.1038/sdata.2018.161

3. Gandhi SA, Kampp J (2015) Skin cancer epidemiology, detection, and management. Med Clin 99(6):1323–1335

4. Rosendahl C, Tschandl P, Cameron A, Kittler H (2011) Diagnostic accuracy of dermatoscopy for melanocytic and nonmelanocytic pigmented lesions. J Am Acad Dermatol 64:1068–1073

5. Argenziano G, Soyer HP (2001) Dermoscopy of pigmented skin lesions–a valuable tool for early diagnosis of melanoma. Lancet Oncol 2(7):443–449

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Skin Cancer Detection using VGG16, InceptionV3 and ResUNet;2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC);2023-07-06

2. Skin Cancer Detection using Ensemble Learning;2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS);2023-06-14

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