The role of AI classifiers in skin cancer images

• Published in: Skin research and technology Vol. 25; no. 5; pp. 750 - 757
• Main Authors: Magalhaes, Carolina, Mendes, Joaquim, Vardasca, Ricardo
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.09.2019
• Subjects: Algorithms; Artificial Intelligence; Bibliographies; Cancer; Classification; Computer vision; Dermoscopy - methods; Diagnosis; Forecasting; Humans; Image analysis; Image classification; Image processing; image processing and computer vision; Information processing; Information retrieval; machine learning; Medical diagnosis; Medical imaging; Medical personnel; Melanoma; Microscopy - methods; Photography - methods; Physicians; Screening; Skin cancer; Skin Neoplasms - diagnosis; Spectrum Analysis - methods; Support vector machines; algorithms; skin cancer; image processing and computer vision; machine learning
• ISSN: 0909-752X; 1600-0846; 1600-0846
• DOI: 10.1111/srt.12713

Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.