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Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms

Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms

Soumen Mukherjee, Arunabha Adhikari, Madhusudan Roy
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 22
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.288542
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MLA

Mukherjee, Soumen, et al. "Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms." IJHISI vol.16, no.4 2021: pp.1-22. http://doi.org/10.4018/IJHISI.288542

APA

Mukherjee, S., Adhikari, A., & Roy, M. (2021). Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-22. http://doi.org/10.4018/IJHISI.288542

Chicago

Mukherjee, Soumen, Arunabha Adhikari, and Madhusudan Roy. "Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-22. http://doi.org/10.4018/IJHISI.288542

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Abstract

This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.