Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma.

Journal: Sensors (Basel, Switzerland)
Published Date: Jan 13, 2023

Abstract

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%.

Authors

  • Adrian D Bandy
    Department of Networks and Digital Media, Kingston University, London KT1 1LQ, UK.
  • Yannis Spyridis
    Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S1 3JD, UK.
  • Barbara Villarini
    School of Computer Science and Engineering, University of Westminster, London W1B 2HW, UK.
  • Vasileios Argyriou
    Department of Networks and Digital Media, Kingston University, London KT1 1LQ, UK.

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