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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.

Pamela Hermosilla¹, Ricardo Soto¹, Emanuel Vega¹

¹Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile.

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Summary

Computer-assisted technology shows promise for early skin cancer detection using dermatoscopic images. Machine learning and deep learning algorithms offer significant advancements, though image quality and expert interpretation remain key challenges.

Keywords:

cancer datasets convolutional neural networks (CNNs)deep learning (DL)machine learning (ML)melanoma skin cancer

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

Dermatology
Medical Imaging
Computer Vision

Background:

Growing interest in computer-assisted technology for early skin cancer detection.
Accuracy of current methods depends on image quality and expert interpretation.
Need for critical assessment of efficacy, challenges, and usability in this field.

Purpose of the Study:

To critically assess the efficacy and challenges of computer-assisted skin cancer detection.
To explain the usability and limitations of current approaches.
To highlight future research directions for the scientific and clinical community.

Main Methods:

Systematic review of 45 contemporary studies.
Analysis of studies from databases like Web of Science and Scopus.
Identification of computer vision techniques for early skin cancer diagnosis, focusing on algorithms, accuracy, and validation metrics.

Main Results:

Significant advancements in skin cancer detection using deep learning and machine learning algorithms.
Identification of various computer vision techniques employed in dermatoscopic image analysis.
Data highlights the potential of AI in improving diagnostic accuracy.

Conclusions:

Computer-assisted technology, particularly machine learning, shows significant potential for early skin cancer detection.
Further research is needed to address challenges related to image quality and interpretation.
Establishes a foundation for future research to enhance the effectiveness of AI in skin cancer diagnosis.