Skin cancer classification model based on VGG 19 and transfer learning

Aburaed, Nour and Panthakkan, Alavikunhu and Al-Saad, Mina and Amin, Saad Ali and Mansoor, Wathiq; (2021) Skin cancer classification model based on VGG 19 and transfer learning. In: 3rd International Conference on Signal Processing and Information Security (ICSPIS). IEEE, Piscataway, N.J.. ISBN 9781728189987 (https://doi.org/10.1109/ICSPIS51252.2020.9340143)

[thumbnail of Aburaed-etal-ICSPIS-2021-Skin-cancer-classification-model-based-on-VGG19]
Preview
 Text. Filename: Aburaed_etal_ICSPIS_2021_Skin_cancer_classification_model_based_on_VGG19.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against Machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer Learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss.

ORCID iDs

Aburaed, Nour ORCID logoORCID: https://orcid.org/0000-0002-5906-0249, Panthakkan, Alavikunhu, Al-Saad, Mina, Amin, Saad Ali and Mansoor, Wathiq;
CORE (COnnecting REpositories)