Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Published Date:

Abstract

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion.

Authors

  • Jorge A Rios-Duarte
    School of Medicine, Universidad de los Andes, Bogotá, Colombia.
  • Andres C Diaz-Valencia
    School of Medicine, Universidad de los Andes, Bogotá, Colombia.
  • Germán Combariza
    Department of Mathematics, Universidad Externado de Colombia, Bogotá, Colombia.
  • Miguel Feles
    Department of Mathematics, Universidad Externado de Colombia, Bogotá, Colombia.
  • Ricardo A Peña-Silva
    School of Medicine, Universidad de los Andes, Bogotá, Colombia.