hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

İLKİN S., Gencturk T. H., Gulagiz F. K., ÖZCAN H., ALTUNCU M. A., ŞAHİN S.

Engineering Science and Technology, an International Journal, vol.24, no.5, pp.1059-1071, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jestch.2021.02.002
  • Journal Name: Engineering Science and Technology, an International Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.1059-1071
  • Keywords: Classification, Skin lesion clustering, Machine learning, Malignom detection, Medical image processing, DIAGNOSIS, IMAGES
  • Kocaeli University Affiliated: Yes


© 2021 Karabuk UniversityMelanoma is a malignant and aggressive type of skin cancer. This paper describes an effective method for detection of melanoma. A hybrid classification algorithm was developed by using the SVM algorithm and a heuristic optimization algorithm. In this algorithm, the SVM algorithm which uses a Gaussian Radial Basis Function (RBF) was enhanced by the Bacterial Colony algorithm (hybSVM). The model was tested with two different datasets namely ISIC and PH2 by using 10 cross fold validation. According to results AUC value of 98%, 97% and an operation time of 26.5, 11.9 sec obtained respectively from ISIC and PH2.