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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...

Assessment of Airway, Skin Color, and Use of Accessory Muscles

Assessment of Airway, Skin Color, and Use of Accessory Muscles

A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...

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Related Experiment Video

Updated: Apr 5, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Adaptive Skin Classification Using Face and Body Detection.

Simone Bianco, Francesca Gasparini, Raimondo Schettini

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 16, 2015

Summary

This summary is machine-generated.

This study introduces an adaptive skin classification method using automatically detected faces and bodies. The approach creates personalized skin classifiers, improving accuracy across diverse skin tones and lighting conditions.

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Skin classification is challenging due to variations in skin tone, race, gender, and lighting.
Existing methods often struggle with these variations, leading to reduced accuracy.
Adaptive approaches are needed to improve the robustness of skin classifiers.

Purpose of the Study:

To develop a novel skin classification method that adapts to individual variations.
To improve the accuracy and robustness of skin classifiers in diverse conditions.
To reduce the dependency of skin classifiers on factors like tan levels, race, gender, and illumination.

Main Methods:

Propose a skin classification method utilizing automatically detected faces and bodies.
Initialize individual ad hoc skin classifiers adaptively based on detected face-body pairs or single faces.
Develop personalized classifiers for each individual in an image.

Main Results:

The proposed method demonstrates superior performance compared to state-of-the-art techniques.
Experimental results on a heterogeneous dataset show statistically significant improvements.
The adaptive initialization effectively handles variations in skin color and environmental conditions.

Conclusions:

The adaptive skin classification method offers a robust solution for diverse populations and conditions.
Personalized classifiers initialized via face and body detection significantly outperform existing methods.
This approach enhances the reliability of skin detection in real-world image analysis applications.