Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review / Grignaffini, Flavia; Barbuto, Francesco; Piazzo, Lorenzo; Troiano, Maurizio; Simeoni, Patrizio; Mangini, Fabio; Pellacani, Giovanni; Cantisani, Carmen; Frezza, Fabrizio. - In: ALGORITHMS. - ISSN 1999-4893. - 15:11(2022), p. 438. [10.3390/a15110438]

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Flavia Grignaffini
Primo
Writing – Original Draft Preparation
;
Lorenzo Piazzo;Maurizio Troiano;Patrizio Simeoni;Fabio Mangini;Giovanni Pellacani;Carmen Cantisani;Fabrizio Frezza
2022

Abstract

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
2022
skin cancer; skin lesion classification; melanoma classification; computer-aided diagnostics; artificial intelligence; machine learning; deep learning; convolutional neural networks
01 Pubblicazione su rivista::01g Articolo di rassegna (Review)
Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review / Grignaffini, Flavia; Barbuto, Francesco; Piazzo, Lorenzo; Troiano, Maurizio; Simeoni, Patrizio; Mangini, Fabio; Pellacani, Giovanni; Cantisani, Carmen; Frezza, Fabrizio. - In: ALGORITHMS. - ISSN 1999-4893. - 15:11(2022), p. 438. [10.3390/a15110438]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1670610
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