IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v16y2021i4p1-22.html
   My bibliography  Save this article

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

Author

Listed:
  • Soumen Mukherjee

    (RCC Institute of Information Technology, India)

  • Arunabha Adhikari

    (West Bengal State University, India)

  • Madhusudan Roy

    (Saha Institute of Nuclear Physics, India)

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.

Suggested Citation

  • Soumen Mukherjee & Arunabha Adhikari & Madhusudan Roy, 2021. "Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-22, October.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-22
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.288542
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yasir Adil Mukhlif & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review," Mathematics, MDPI, vol. 12(7), pages 1-29, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.