Deep convolutional neural network (DCNN) for skin cancer classification

Nour Aburaed, Alavikunhu Panthakkan, Mina Al-Saad, Saad Ali Amin, Wathiq Mansoor

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Skin cancer is one of the most threatening types of cancer, with an increasing rates throughout the decade. Detecting and classifying skin cancer in its early stages provides better chances for treatment. In the recent years, Convolutional Neural Networks (CNNs) emerged as a powerful solution that aids the diagnosis of skin cancer. In this paper, Human Against Machine (HAM) 10000 dataset is used to demonstrate skin cancer classification strategy. VGG16, VGG19, and a Deep CNN proposed in this paper are implemented, trained, and evaluated. The dataset pre-processing steps and methodology are illustrated, and the network parameters and training process are explained. The performance of all three networks are compared in terms of the average overall accuracy and loss.

Original languageEnglish
Title of host publicationICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings
Place of PublicationPiscataway, N.J.
PublisherIEEE
ISBN (Electronic)9781728160443
DOIs
Publication statusPublished - 23 Nov 2020
Event27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020 - Glasgow, United Kingdom
Duration: 23 Nov 202025 Nov 2020

Conference

Conference27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period23/11/2025/11/20

Keywords

  • biomedical imaging
  • classification
  • convolutional neural networks
  • deep learning
  • image processing
  • skin cancer

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