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V. J. Peter and V. G. N. K. Babu, “Skin cancer detection using support vector machine with histogram of oriented gradients features,” ICTACT Journal on Soft Computing, vol. 11, no. 2, pp. 2301–2305, 2021.

M. Dildar, S. Akram, M. Irfan et al., “Skin cancer detection: a review using deep learning techniques,” International Journal of Environmental Research and Public Health, vol. 18, no. 10, pp. 1–22, 2021.

B. P. Vrigazova, “Detection of malignant and benign breast cancer using the Anova-Bootstrap-SVM,” Journal of Data and Information Science, vol. 5, no. 2, pp. 62–75, 2020.

National Cancer Registration and Analysis Service (Public Health England), Organization of Cancer Research UK, London, United Kingdom, 2019

A. Esteva, B. Kuprel, and R. A. Novoa, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 546, no. 686, 2017.

MehwishDildar ,ShumailaAkram, Muhammad Irfan, Hikmat Ullah Khan, Muhammad Ramzan, Abdur Rehman Mahmood, Soliman AyedAlsaiari , Abdul Hakeem M Saeed, Mohammed OlaythahAlraddadia, Mater HussenMahnashiSkin Cancer Detection: A Review Using Deep Learning Techniques. International Journal of Environmental Research and Public Health, May-20, 2021

Mohammed Rakeibul Hasan, Mohammed IshraafFatemi, Mohammad Monirujjaman Khan,Manjit Kaur, and Atef ZaguiaComparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks, Journal of Healthcare Engineering vol. 2021,

Catarina Barata, M. Emre Celebi, Jorge S. Marques,A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer. IEEE Journal of Biomedical and Health Informatics, vol.23 no.3, pp 1096-1109,August 2015.

M.H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S.M.R. Soroushmehr, K. Ward, K. Najarian,Skin Lesion Segmentation in Clinical Images Using Deep Learning, 2016 23rd International Conference on Pattern Recognition (ICPR) Cancun Center, Cancun, México, December 4-8, 2016.

Voganathan.M, Umesh kumar.E, Babu.TClassification of Skin Lesions in Digital Images for the Diagnosis of Skin Cancer, Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC 2020) IEEE Explore Part Number: CFP20V90-ART; ISBN: 978-1-7281-5461-9, 2020

Zekry, Abdelhalim. Deep Learning Can Improve Early Skin Cancer Detection.International Journal of Electronics and Telecommunications. Vol. 65, no. 3pp. 507-512, 2019.

Q. Li, L. Chang, H. Liu, M. Zhou, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol., vol. 74, pp. 79–86, 2015.

R. J. Friedman, D. S. Rigel, and A. W. Kopf, “Early Detection of Malignant Melanoma : The Role Of PhysicianExamination and of the Skin,” 1985..

Rashid H., Tanveer M.A., Aqeel Khan H. Skin Lesion Classification Using GAN Based Data Augmentation; Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Berlin, Germany. 23–27 July 2019; pp. 916–919.

Yu L., Chen H., Dou Q., Qin J., Heng P.-A. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks. IEEE Trans. Med. Imaging. 2017;36:994–1004. doi: 10.1109/TMI.2016.2642839.

V.Nwogu and I. Singh, “Analyzing skin lesions in dermo- scopy images using convolutional neural networks,” in Pro-ceedings of the 2018 IEEE International Conference on Systems,Man, and Cybernetics (SMC), pp. 4035–4040, Miyazaki, Japan,2018.

E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H. Jafari, K. Ward, K. Najarian,“Melanoma Detection by Analysis of Clinical Images Using Convolutional Neural Network,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Florida, 2016.

S. Kalouche, “Vision-based classification of skin cancer usingdeep learning,” pp. 1–6, 2016, https://www.semanticscholar.org/paper/Vision-Based-Classification-of-Skin-Cancer-usingKalouche/b57ba909756462d812dc20fca157b3972bc1f533[Online] Available:.

American Cancer Society. Cancer facts & figures 2016. Atlanta, American Cancer Society 2016.

Rogers, H. W. et al. Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012. JAMA Dermatology 151.10, 1081–1086 (2015).

Stern, R. S. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146, 279–282 (2010).

L.L. Chen, S.W. Dusza, N. Jaimes, et al., “Performance of the primary step of the 2-step dermoscopy rule”, JAMA Dermatol, 151(7), 715-21, 2015.

A. Esteva, B. Kuprel, R.A. Novoa, et al, “Dermatologist level classification of carcinoma with deep neural networks”. Nature 542, 115–118, 2017.

Pieter Van Tree, Miguel First State Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Baronet Dhoedt et al., “Visualizing Convolutional Neural Networks to boost call Support for Skin Lesion Classification, arXiv:1809.03851v1 [cs.CV] eleven Sept 2018.

Fabbrocini. G, Triassi. M, Mauriello. M.C, Torre G., Annunziata M.C., De Vita V., et al., “Epidemiology of skin cancer: Role of some environmental factors”, Cancers (Basel) 2010 Nov 24;2(4):1980-1989 [FREE Full text] [doi: 10.3390/cancers2041980] [Medline: 24281212].

S M J, P M, Aravindan C, Appavu R. Classification of skin cancer from dermoscopic images using deep neural network architectures. Multimed Tools Appl. 2023;82(10):15763-15778. doi: 10.1007/s11042-022-13847-3. Epub 2022 Oct 12. PMID: 36250184; PMCID: PMC9554840.

Wu S, Cho E, Li WQ, Weinstock MA, Han J, Qureshi AA. History of Severe Sunburn and Risk of Skin Cancer Among Women and Men in 2 Prospective Cohort Studies. Am J Epidemiol. 2016 May 1;183(9):824-33. doi: 10.1093/aje/kwv282. Epub 2016 Apr 3. PMID: 27045074; PMCID: PMC4851991.

Yamashita, R., Nishio, M., Do, R.K.G. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

Published

26.03.2024

How to Cite

author1, author2. (2024). Title. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1275–1282. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5594

Issue

Vol. 12 No. 21s (2024)

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Research Article

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