533 519
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 8 , Issue 1, PP: 33-42 , 2023 | Cite this article as | XML | Html |PDF

Title

Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms

  Ahmed Abdelhafeez 1 * ,   Hoda K. Mohamed 2

1  Faculty of Information Systems and Computer Science, October 6th University, Cairo, 12585, Egypt
    (aahafeez.scis@o6u.edu.eg)

2  Faculty of Engineering, Ain shams University, Cairo, 11566, Egypt
    (Hoda.korashy@eng.asu.edu.eg)


Doi   :   https://doi.org/10.54216/JISIoT.080103

Received: May 15, 2022 Accepted: January 23, 2023

Abstract :

Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC.

Keywords :

Skin cancer; Neutrosophic set; Clustering; Neutrosophic c-means clustering; Fuzzy c-means clustering

References :

[1]       A. G. C. Pacheco and R. A. Krohling, “The impact of patient clinical information on automated skin cancer detection,” Computers in biology and medicine, vol. 116, p. 103545, 2020.

[2]       M. A. Kadampur and S. Al Riyaee, “Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images,” Informatics in Medicine Unlocked, vol. 18, p. 100282, 2020.

[3]       D. N. H. Thanh, V. B. S. Prasath, L. M. Hieu, and N. N. Hien, “Melanoma skin cancer detection method based on adaptive principal curvature, color normalization and feature extraction with the ABCD rule,” Journal of Digital Imaging, vol. 33, pp. 574–585, 2020.

[4]       F. W. Kong, C. Horsham, A. Ngoo, H. P. Soyer, and M. Janda, “Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time?” International Journal of Dermatology, vol. 60, no. 3, pp. 289–308, 2021.

[5]       O. T. Jones et al., “Artificial intelligence and machine learning algorithms for early detection of skin cancer in the community and primary care settings: a systematic review,” The Lancet Digital Health, vol. 4, no. 6, pp. e466–e476, 2022.

[6]       H. Nahata and S. P. Singh, “Deep learning solutions for skin cancer detection and diagnosis,” Machine Learning with Health Care Perspective: Machine Learning and Healthcare, pp. 159–182, 2020.

[7]       M. Kumar, M. Alshehri, R. AlGhamdi, P. Sharma, and V. Deep, “A de-ann inspired skin cancer detection approach using fuzzy c-means clustering,” Mobile Networks and Applications, vol. 25, no. 4, pp. 1319–1329, 2020.

[8]       M. Dildar et al., “Skin cancer detection: a review using deep learning techniques,” International journal of environmental research and public health, vol. 18, no. 10, p. 5479, 2021.

[9]       R. Ashraf et al., “Region-of-interest based transfer learning assisted framework for skin cancer detection,” IEEE Access, vol. 8, pp. 147858–147871, 2020.

[10]     X. Dai, I. Spasić, B. Meyer, S. Chapman, and F. Andres, “Machine learning on mobile: An on-device inference app for skin cancer detection,” in 2019 fourth international conference on fog and mobile edge computing (FMEC), 2019, pp. 301–305.

[11]     Nechirvan Asaad Zebari , Mehmet Emin Tenekeci, Support System Based Computer-Aided Detection for Skin Cancer: A Review, Fusion: Practice and Applications, Vol. 7 , No. 1, 30-40, 2022

[12]     Fatma Taher , Ahmed Abdelaziz, Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification, International Journal of Neutrosophic Science, Vol. 19, No. 1, 177-187, 2022

[13]     A. Ameri, “A deep learning approach to skin cancer detection in dermoscopy images,” Journal of Biomedical Physics and Engineering, vol. 10, no. 6, pp. 801–806, 2020.

[14]     L. Wei, K. Ding, and H. Hu, “Automatic skin cancer detection in dermoscopy images based on ensemble lightweight deep learning network,” IEEE Access, vol. 8, pp. 99633–99647, 2020.

[15]     A. Khamparia, P. K. Singh, P. Rani, D. Samanta, A. Khanna, and B. Bhushan, “An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, p. e3963, 2021.

[16]     R. Mohakud and R. Dash, “Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 6280–6291, 2022.

[17]     A. Murugan, S. A. H. Nair, and K. P. Kumar, “Detection of skin cancer using SVM, random forest and kNN classifiers,” Journal of medical systems, vol. 43, no. 8, pp. 1–9, 2019.

[18]     Y. Guo and A. Sengur, “NCM: Neutrosophic c-means clustering algorithm,” Pattern Recognition, vol. 48, no. 8, pp. 2710–2724, 2015.

[19]     Y. Akbulut, A. Şengür, Y. Guo, and K. Polat, “KNCM: Kernel neutrosophic c-means clustering,” Applied Soft Computing, vol. 52, pp. 714–724, 2017.

[20]     Y. Guo, R. Xia, A. Şengür, and K. Polat, “A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering,” Neural Computing and Applications, vol. 28, no. 10, pp. 3009–3019, 2017.

[21]     S. Askari, “Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development,” Expert Systems with Applications, vol. 165, p. 113856, 2021.

[22]     C. L. Chowdhary, M. Mittal, P. A. Pattanaik, and Z. Marszalek, “An efficient segmentation and classification system in medical images using intuitionist possibilistic fuzzy C-mean clustering and fuzzy SVM algorithm,” Sensors, vol. 20, no. 14, p. 3903, 2020.

[23]     P. K. Mishro, S. Agrawal, R. Panda, and A. Abraham, “A novel type-2 fuzzy C-means clustering for brain MR image segmentation,” IEEE Transactions on Cybernetics, vol. 51, no. 8, pp. 3901–3912, 2020.

[24]     R. Rout, P. Parida, Y. Alotaibi, S. Alghamdi, and O. I. Khalaf, “Skin lesion extraction using multiscale morphological local variance reconstruction based watershed transform and fast fuzzy C-means clustering,” Symmetry, vol. 13, no. 11, p. 2085, 2021.

[25]     Y. Guo and A. Sengur, “NECM: Neutrosophic evidential c-means clustering algorithm,” Neural Computing and Applications, vol. 26, pp. 561–571, 2015.

[26]     B. Ji, X. Hu, F. Ding, Y. Ji, and H. Gao, “An effective color image segmentation approach using superpixel-neutrosophic C-means clustering and gradient-structural similarity,” Optik, vol. 260, p. 169039, 2022.

[27]     A. M. Anter, A. E. Hassanien, M. A. A. ElSoud, and M. F. Tolba, “Neutrosophic sets and fuzzy c-means clustering for improving ct liver image segmentation,” in Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, 2014, pp. 193–203.

[28]     M. K. Alsmadi, “A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 697–706, 2018.

[29]     Y. Guo and A. Sengur, “A novel color image segmentation approach based on neutrosophic set and modified fuzzy c-means,” Circuits, Systems, and Signal Processing, vol. 32, pp. 1699–1723, 2013.

[30]     H. D. Cheng, Y. Guo, and Y. Zhang, “A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm,” New Mathematics and Natural Computation, vol. 7, no. 01, pp. 155–171, 2011.

[31]     A. S. Ashour, Y. Guo, E. Kucukkulahli, P. Erdogmus, and K. Polat, “A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation,” Applied Soft Computing, vol. 69, pp. 426–434, 2018.

[32]     Y. Guo and H.-D. Cheng, “New neutrosophic approach to image segmentation,” Pattern Recognition, vol. 42, no. 5, pp. 587–595, 2009.

[33]     R. Barona and E. A. M. Anita, “Optimal cryptography scheme and efficient neutrosophic C-means clustering for anomaly detection in the cloud environment,” Journal of Circuits, Systems, and Computers, vol. 30, no. 05, p. 2150084, 2021.

[34]     M. N. Qureshi and M. V. Ahamad, “An improved method for image segmentation using K-means clustering with neutrosophic logic,” Procedia computer science, vol. 132, pp. 534–540, 2018.

[35]     A. M. Anter and A. E. Hassenian, “CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm,” Artificial intelligence in medicine, vol. 97, pp. 105–117, 2019.

[36]     Z. Lu, Y. Qiu, and T. Zhan, “Neutrosophic C-means clustering with local information and noise distance-based kernel metric image segmentation,” Journal of Visual Communication and Image Representation, vol. 58, pp. 269–276, 2019.

[37]     D. S. Irene and T. Sethukarasi, “Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification,” Journal of Circuits, Systems, and Computers, vol. 29, no. 16, p. 2050260, 2020.

[38]     F. Zamani, M. H. Olyaee, and A. Khanteymoori, “NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering,” BMC bioinformatics, vol. 21, no. 1, pp. 1–15, 2020.


Cite this Article as :
Style #
MLA Ahmed Abdelhafeez, Hoda K. Mohamed. "Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms." Journal of Intelligent Systems and Internet of Things, Vol. 8, No. 1, 2023 ,PP. 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)
APA Ahmed Abdelhafeez, Hoda K. Mohamed. (2023). Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, 8 ( 1 ), 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)
Chicago Ahmed Abdelhafeez, Hoda K. Mohamed. "Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms." Journal of Journal of Intelligent Systems and Internet of Things, 8 no. 1 (2023): 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)
Harvard Ahmed Abdelhafeez, Hoda K. Mohamed. (2023). Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, 8 ( 1 ), 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)
Vancouver Ahmed Abdelhafeez, Hoda K. Mohamed. Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 8 ( 1 ): 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)
IEEE Ahmed Abdelhafeez, Hoda K. Mohamed, Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 33-42 (Doi   :  https://doi.org/10.54216/JISIoT.080103)