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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: Jun 12, 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

IoT-Driven Skin Cancer Detection: Active Learning and Hyperparameter Optimization for Enhanced Accuracy.

Jing Yang, Haoshen Qin, Jinli Wang

IEEE Journal of Biomedical and Health Informatics

|June 10, 2025

Summary

This summary is machine-generated.

This study introduces an advanced active learning framework using deep reinforcement learning for efficient skin cancer detection. The novel approach enhances diagnostic accuracy while minimizing the need for large labeled datasets.

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Last Updated: Jun 12, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Computational Biology

Background:

Skin cancer diagnosis is challenging due to lesion variability.
Deep learning (DL) aids automated diagnosis but requires extensive labeled data.
Internet of Things (IoT) enables real-time healthcare data exchange.

Purpose of the Study:

To develop an innovative active learning (AL) framework for improved skin cancer detection.
To reduce the dependency on large, labeled datasets for deep learning models.
To enhance the efficiency and accuracy of early skin cancer diagnosis.

Main Methods:

Implemented a deep reinforcement learning (DRL) strategy for dynamic sample selection in active learning.
Introduced a novel scope loss function to balance data exploitation and exploration.
Utilized an enhanced artificial bee colony (ABC) algorithm for hyperparameter optimization.

Main Results:

Achieved high accuracy on benchmark datasets: 92.791% F-measure on ISIC and 91.984% on HAM10000.
Demonstrated the framework's effectiveness in optimizing classification with reduced labeled data.
Showcased the benefits of DRL-driven sample selection and the scope loss function.

Conclusions:

The proposed AL framework significantly advances early skin cancer detection capabilities.
This approach offers a reliable and efficient tool for healthcare professionals in diagnosing skin lesions.
The integration of DRL and novel loss functions presents a promising direction for medical image analysis.