IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v16y2021i1p16-37.html
   My bibliography  Save this article

Computer-Aided Diagnosis for Spitzoid Lesions Classification Using Artificial Intelligence Techniques

Author

Listed:
  • Abir Belaala

    (LINFI Laboratory, Biskra University, Algeria)

  • Labib Sadek Terrissa

    (LINFI Laboratory, Biskra University, Algeria)

  • Noureddine Zerhouni

    (FEMTO-ST Institute, University Bourgogne Franche-Comte, France & École Nationale Supérieure de Mécanique et des Microtechniques, Besançon, France)

  • Christine Devalland

    (Service of Anatomy and Pathology Cytology, France)

Abstract

Spitzoid lesions may be largely categorized into Spitz Nevus, Atypical Spitz Tumors, and Spitz Melanomas. Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz Nevus and malignant Spitz Melanomas. Specifically, this paper aims to test several artificial intelligence techniques so as to build a computer-aided diagnosis system. A proposed three-phase approach is being implemented. In Phase 1, collected data are preprocessed with an effective SMOTE-based method being implemented to treat the imbalance data problem. Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase 2. Finally, in Phase 3, a 10-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification. Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy, specificity (0.98), and a sensitivity of 0.99.

Suggested Citation

  • Abir Belaala & Labib Sadek Terrissa & Noureddine Zerhouni & Christine Devalland, 2021. "Computer-Aided Diagnosis for Spitzoid Lesions Classification Using Artificial Intelligence Techniques," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(1), pages 16-37, January.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:1:p:16-37
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2021010102
    Download Restriction: no
    ---><---

    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:1:p:16-37. 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.