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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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Related Experiment Video

Updated: Oct 23, 2025

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

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

Kiyotaka Wada^1,2, Mutsumi Watanabe², Masahiro Shinchi²

¹Medipolis Proton Therapy and Research Center.

Nihon Hoshasen Gijutsu Gakkai Zasshi

|August 23, 2021

Summary

A new deep learning system accurately grades radiation dermatitis, a common side effect of radiotherapy. This AI-powered tool enhances objective assessment, improving patient care by providing reliable radiation dermatitis grading support.

Keywords:

Poisson image editing deep learning radiation dermatitis radiation dermatitis grading support system radiotherapy

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Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

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Last Updated: Oct 23, 2025

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Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

Published on: May 27, 2016

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Radiation dermatitis is a frequent complication of radiotherapy.
Current visual assessment using Common Terminology Criteria for Adverse Events (CTCAE) lacks objectivity.
Objective evaluation methods are needed to accurately grade radiation dermatitis severity.

Purpose of the Study:

To develop and evaluate a radiation dermatitis grading support system (RDGS).
To utilize a deep convolutional neural network (DCNN) for objective assessment.
To improve the accuracy of radiation dermatitis grading.

Main Methods:

A DCNN was trained on 647 clinical skin images of radiation dermatitis (Grades 1-4).
A hybrid data generation method combining image conversion and Poisson image editing was used.
The system, Hyb-RDGS, was evaluated for its classification accuracy.

Main Results:

The Hyb-RDGS achieved an overall accuracy of 85.1%.
This accuracy surpassed that of conventional data augmentation techniques.
The results indicate superior performance in classifying radiation dermatitis grades.

Conclusions:

The Hyb-RDGS demonstrates effectiveness, particularly with Poisson image editing.
This system offers a potential solution for objective evaluation in radiation dermatitis grading.
The study suggests a promising AI-driven approach to support clinical decision-making.