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Towards an Effective Imaging-Based Decision Support System for Skin Cancer

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

Ricardo Vardasca, Carolina Magalhaes
Copyright: © 2023 |Pages: 29
ISBN13: 9781668475447|ISBN10: 1668475448|EISBN13: 9781668475454
DOI: 10.4018/978-1-6684-7544-7.ch067
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MLA

Vardasca, Ricardo, and Carolina Magalhaes. "Towards an Effective Imaging-Based Decision Support System for Skin Cancer." Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, IGI Global, 2023, pp. 1305-1333. https://doi.org/10.4018/978-1-6684-7544-7.ch067

APA

Vardasca, R. & Magalhaes, C. (2023). Towards an Effective Imaging-Based Decision Support System for Skin Cancer. In I. Management Association (Ed.), Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention (pp. 1305-1333). IGI Global. https://doi.org/10.4018/978-1-6684-7544-7.ch067

Chicago

Vardasca, Ricardo, and Carolina Magalhaes. "Towards an Effective Imaging-Based Decision Support System for Skin Cancer." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, 1305-1333. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7544-7.ch067

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Abstract

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct applications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suffers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.

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