Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.

Journal: BMC medical informatics and decision making
Published Date: Jul 1, 2025

Abstract

BACKGROUND AND OBJECTIVE: Early detection and classification of skin cancer are critical for improving patient outcomes. Dermoscopic image analysis using Computer-Aided Diagnostics (CAD) is a powerful tool to assist dermatologists in identifying and classifying skin lesions. Traditional machine learning models require extensive feature engineering, which is time-consuming and less effective in handling complex data like skin lesions. This study proposes a deep learning-based network-level fusion architecture that integrates multiple deep models to enhance the classification and localization of skin lesions in dermoscopic images. The goal is to address challenges like irregular lesion shapes, inter-class similarities, and class imbalances while providing explainability through artificial intelligence.

Authors

  • Mehak Arshad
    Department of Computer Science, HITEC University Taxila, Taxila, Pakistan.
  • Muhammad Attique Khan
    Department of Computer Science, HITEC University, Taxila, Pakistan.
  • Nouf Abdullah Almujally
    Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Areej Alasiry
    College of Computer Science, King Khalid University, Abha, 61413, Saudi Arabia.
  • Mehrez Marzougui
    College of Computer Science, King Khalid University, Abha, 61413, Saudi Arabia.
  • Yunyoung Nam

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