Skin lesion segmentation using deep learning algorithm with ant colony optimization.

Journal: BMC medical informatics and decision making
Published Date: Sep 27, 2024

Abstract

BACKGROUND: Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis.

Authors

  • Nadeem Sarwar
    Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan. Nadeem_srwr@yahoo.com.
  • Asma Irshad
    School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan. asmairshad76@yahoo.com.
  • Qamar H Naith
    Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, PO Box 34, Jeddah, 21959, Saudi Arabia.
  • Kholod D Alsufiani
    Computer Sciences Program, Turabah University College, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
  • Faris A Almalki
    Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

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