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A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

V. Vidya Lakshmi1,*, J. S. Leena Jasmine2

1 Research Scholar, Anna University, Chennai, Tamil Nadu, 600025, India
2 Velammal Engineering college, Chennai, Tamil Nadu, 600066, India

* Corresponding Author: V. Vidya Lakshmi. Email: email

Computer Systems Science and Engineering 2021, 37(2), 233-245.


Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical image. Thus the proposed HAIM uses Curvelet (CurT), Contourlet (ConT) and Shearlet (SheT) transforms as feature extraction techniques. Though MLP is very flexible and well suitable for the classification problem, the learning of weights is a challenging task. Also, the optimization process does not converge, and the model may not be stable. To overcome these drawbacks, EWHMLP is developed. Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33% in a multi-class approach on PH2 database.


Cite This Article

APA Style
Lakshmi, V.V., Jasmine, J.S.L. (2021). A hybrid artificial intelligence model for skin cancer diagnosis. Computer Systems Science and Engineering, 37(2), 233-245.
Vancouver Style
Lakshmi VV, Jasmine JSL. A hybrid artificial intelligence model for skin cancer diagnosis. Comput Syst Sci Eng. 2021;37(2):233-245
IEEE Style
V.V. Lakshmi and J.S.L. Jasmine, "A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis," Comput. Syst. Sci. Eng., vol. 37, no. 2, pp. 233-245. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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