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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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Artificial Intelligence for Skin Cancer Detection: Scoping Review.

Abdulrahman Takiddin^1,2, Jens Schneider², Yin Yang²

¹Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States.

Journal of Medical Internet Research

|November 25, 2021

Summary

Artificial intelligence (AI) aids skin cancer diagnosis, but model reliability varies. Studies using smaller datasets reported higher accuracy, questioning the dependability of these AI tools for skin cancer detection.

Keywords:

artificial intelligence deep neural networks machine learning skin cancer skin lesion

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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 Informatics
Computer Science

Background:

Skin cancer is a prevalent human malignancy.
Traditional diagnostic methods are time-consuming, expensive, and require expert physicians.
Artificial intelligence (AI) offers a promising alternative for skin cancer detection and classification.

Purpose of the Study:

To systematically identify and categorize AI-based technologies for skin cancer detection.
To evaluate the reliability of AI models by analyzing dataset size and diagnostic class impact on performance metrics.

Main Methods:

A systematic literature search was conducted across IEEE Xplore, ACM DL, and Ovid MEDLINE databases.
Studies were selected based on relevance to skin cancer, AI utilization, and diagnostic capabilities, adhering to PRISMA-ScR guidelines.
Data extraction and synthesis were performed by two independent reviewers, grouping studies by AI technique and evaluation metrics.

Main Results:

Out of 906 retrieved papers, 53 met the inclusion criteria.
Shallow AI techniques were employed in 14 studies, while deep AI techniques were used in 39 studies.
Accuracy was the primary metric in 39 studies, with smaller datasets generally yielding higher reported accuracy.

Conclusions:

AI models show potential in skin cancer detection, but direct comparison is challenging due to varied metrics and image types.
Factors like dataset size and number of diagnostic classes significantly influence AI model performance.
The reliability of high-accuracy AI models is questionable when trained on limited data, necessitating cautious interpretation of results.