Relevant bibliographies by topics / Skin cancer detection

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  1. Journal articles

Academic literature on the topic 'Skin cancer detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Skin cancer detection"

1

A, Soujanya. "A Review on Melanoma Skin Cancer Detection Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1525–33. http://dx.doi.org/10.5373/jardcs/v12sp7/20202255.

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2

Harte, M., and G. Knepil. "Skin cancer detection." British Dental Journal 227, no. 7 (2019): 539. http://dx.doi.org/10.1038/s41415-019-0808-3.

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3

M, Vijayalakshmi M. "Melanoma Skin Cancer Detection using Image Processing and Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 780–84. http://dx.doi.org/10.31142/ijtsrd23936.

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4

Shinde, Prof S. G. "Skin Cancer Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3865–71. http://dx.doi.org/10.22214/ijraset.2022.44642.

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Abstract: Skin cancer is one of the most popular types of cancer, which inspires the life of millions of people every year in the entire world. Melanoma is one of the forms of cancer that initiates in melanocytes and it can influence the skin only. It’s more serious as compare with other types of skin cancer. The Melanoma can be of benign or malignant. The paper focused on detection system has been designed for diagnosing melanoma in early stages by using digital image processing techniques. The paper has many steps like preprocessing, segmentation, feature extraction and detection process whi
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5

Biro, Laszlo, Ely Price, and Alfredo J. Brand. "Skin cancer detection clinics." Journal of the American Academy of Dermatology 12, no. 2 (1985): 375. http://dx.doi.org/10.1016/s0190-9622(85)80067-0.

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6

Dorrell, Deborah N., and Lindsay C. Strowd. "Skin Cancer Detection Technology." Dermatologic Clinics 37, no. 4 (2019): 527–36. http://dx.doi.org/10.1016/j.det.2019.05.010.

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7

Thompson, Lewis W. "Skin cancer—early detection." Seminars in Surgical Oncology 5, no. 3 (1989): 153–62. http://dx.doi.org/10.1002/ssu.2980050303.

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8

Khatri, Bhavay. "Skin Cancer Detection: A Survey." International Journal of Research in Science and Technology 13, no. 01 (2023): 01–03. http://dx.doi.org/10.37648/ijrst.v13i01.001.

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Due to a lack of awareness of its warning signs and preventative measures, skin cancer—one of the deadliest types of cancer—has seen a significant increase in mortality rates. Therefore, early detection at an early stage is essential to halting the spread of cancer. Although there are other types of skin cancer, melanoma is the most dangerous. However, melanoma patients have a 96% survival rate when detected early with straightforward and cost-effective treatments. The project aims to classify various kinds of skin cancer using image processing and machine learning. Melanoma is a type of skin
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9

de Souza Ganzeli, Heitor, Julia Godoy Bottesini, Leandro de Oliveira Paz, and Matheus Figueiredo Salgado Ribeiro. "SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques." IEEE Latin America Transactions 9, no. 2 (2011): 206–12. http://dx.doi.org/10.1109/tla.2011.5765575.

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10

Devi, M. Shyamala, A. N. Sruthi, and P. Balamurugan. "Artificial neural network classification-based skin cancer detection." International Journal of Engineering & Technology 7, no. 1.1 (2017): 591. http://dx.doi.org/10.14419/ijet.v7i1.1.10364.

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At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. I
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