Deep Learning and Few-Shot Learning in the Detection of Skin Cancer: An Overview

Abstract

AbstractSkin cancer is a severe condition that should be detected early. The two most prevalent types of skin cancer include melanoma and non-melanoma. Melanoma has been identified as the utmost dangerous skin cancer. Yet, discriminating melanoma lesions from non-melanoma lesions has proven challenging. Several artificial intelligence-based strategies have been introduced in the literature to handle skin cancer detection, including deep learning and few-shot learning strategies. According to the evidence in the literature, deep learning algorithms are reported to perform well when trained on large datasets. However, they are only effective when the target domain has enough labeled samples; they do not ensure adequate network activation variables to adjust to new target regions rapidly when the target domain has insufficient data. Consequently, few-shot learning paradigms have been presented in the literature to promote learning from such limited amounts of labeled data. A search on PubMed from inception to 7 June 2022 for studies investigating the review of the application of deep learning and few-shot learning in the detection of skin cancer was performed via the use of title terms “Deep Learning” AND “Few-Shot Learning” AND “Skin Cancer Detection” AND “Review,” combined with title terms or MeSH terms “Deep Learning” AND “Few-Shot Learning” AND “Skin Cancer Detection” AND “Review,” with no limits on language or date of publication. We found no paper that has reviewed the application of deep learning and few-shot learning in detecting skin cancer. This paper, therefore, presents a brief overview of some of the most critical applications of deep learning and few-shot learning schemes in the detection of skin cancer lesions from skin image data.KeywordsArtificial intelligenceDeep learningFew-shot learningMelanomaSkin cancer detection