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Automatic Detection of Skin Cancer Melanoma Using Transfer Learning in Deep Network

Wang, Xuyiling; Yang, Ying; Mandal, Bappaditya

Authors

Xuyiling Wang

Bappaditya Mandal



Abstract

As the deadliest type of skin cancer, melanoma has a high mortality rate and takes away thousands of lives in the UK every year. However, if detected at earlier stage, the survival rate largely increases. With the development of machine learning, many well-known pre-trained models were used to detect melanoma accurately through imaging analysis. The overall performance is far beyond skillful human experts. This paper examined the performance of a pre-trained model—Visual Geometry Group network (VGG) on International Skin Imaging Collaboration (ISIC) 2019 challenge dataset in automatically classifying melanoma and non-melanoma diseases. The highest accuracy achieved was 0.9067 with AU ROC over 0.93. Ablation studies illustrated potential factors that could affect model performance, including training data size, frozen layers, classifier nodes and data augmentation methods.

Journal Article Type Conference Paper
Conference Name INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING (ICoBE 2021)
Conference Location Perlis, Malaysia
Acceptance Date Feb 21, 2023
Online Publication Date Feb 21, 2023
Publication Date 2023
Deposit Date Oct 10, 2023
Journal INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, ICOBE 2021
Print ISSN 0094-243X
Publisher AIP Publishing
Peer Reviewed Peer Reviewed
Volume 2562
Article Number ARTN 020009
DOI https://doi.org/10.1063/5.0111909