Welcome to the InfoSci Platform
Reference Hub6
Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network

Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network

Ananjan Maiti, Biswajoy Chatterjee, K. C. Santosh
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 23
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860280|DOI: 10.4018/IJACI.2021070104
Cite Article Cite Article

MLA

Maiti, Ananjan, et al. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network." IJACI vol.12, no.3 2021: pp.75-97. http://doi.org/10.4018/IJACI.2021070104

APA

Maiti, A., Chatterjee, B., & Santosh, K. C. (2021). Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network. International Journal of Ambient Computing and Intelligence (IJACI), 12(3), 75-97. http://doi.org/10.4018/IJACI.2021070104

Chicago

Maiti, Ananjan, Biswajoy Chatterjee, and K. C. Santosh. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.3: 75-97. http://doi.org/10.4018/IJACI.2021070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. The experimental images have combined images of naevus (1500), melanoma (1000), and basal cell carcinoma (500). The proposed methods handled issues like class imbalanced with generative adversarial networks (GAN). The recommended color quantization method with synthetic data generation increased the accuracy of the popular machine learning models as it gives an accuracy of 97.08% in random forest. The proposed model preserves a decent accuracy with KNN, adaboost, and gradient boosting.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.