Machine Learning and Its Application in Skin Cancer

Kinnor Das, Clay J. Cockerell, Anant Patil, Paweł Pietkiewicz, Mario Giulini, Stephan Grabbe and Mohamad Goldust
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Kinnor Das: Department of Dermatology Venereology and Leprosy, Silchar Medical College, Silchar 788014, India
Clay J. Cockerell: Departments of Dermatology and Pathology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Anant Patil: Department of Pharmacology, Dr. DY Patil Medical College, Navi Mumbai 400706, India
Paweł Pietkiewicz: Surgical Oncology and General Surgery Clinic I, Greater Poland Cancer Center, 61-866 Poznan, Poland
Mario Giulini: Department of Dermatology, University Medical Center Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
Stephan Grabbe: Department of Dermatology, University Medical Center Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
Mohamad Goldust: Department of Dermatology, University Medical Center Mainz, Langenbeckstraße 1, 55131 Mainz, Germany

IJERPH, 2021, vol. 18, issue 24, 1-10

Abstract: Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. Although it has a significant role in the detection of skin cancer, dermatology skill lags behind radiology in terms of AI acceptance. With continuous spread, use, and emerging technologies, AI is becoming more widely available even to the general population. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. Early detection is key for the effective treatment and better outcomes of skin cancer. Specialists can accurately diagnose the cancer, however, considering their limited numbers, there is a need to develop automated systems that can diagnose the disease efficiently to save lives and reduce health and financial burdens on the patients. ML can be of significant use in this regard. In this article, we discuss the fundamentals of ML and its potential in assisting the diagnosis of skin cancer.

Keywords: artificial intelligence; machine learning; skin cancer (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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