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

Wang, Xuyiling; Yang, Ying; Mandal, Bappaditya

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.

Citation

Wang, X., Yang, Y., & Mandal, B. (2023). Automatic Detection of Skin Cancer Melanoma Using Transfer Learning in Deep Network. AIP conference proceedings, 2562, Article ARTN 020009. https://doi.org/10.1063/5.0111909

• 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