Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Aug 16, 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

An Optimization-Based Technology Applied for Face Skin Symptom Detection.

Yuan-Hsun Liao¹, Po-Chun Chang¹, Chun-Cheng Wang¹

¹Department of Computer Science, Tunghai University, Taichung 407224, Taiwan.

Healthcare (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Application and effectiveness of adaptive AI in elderly healthcare.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society·2024

Same journal

Correction: Haddock et al. <i>Imagine the Possibilities Pain Coalition</i> and Opioid Marketing to Veterans: Lessons for Military and Veterans Healthcare. <i>Healthcare</i> 2025, <i>13</i>, 434.

Healthcare (Basel, Switzerland)·2026

Same journal

Macro Responsibility in the Microvascular World: Nurse Experiences in Flap Care, a Phenomenological Study.

Healthcare (Basel, Switzerland)·2026

Same journal

Agreement Between Standing Eight-Point Multifrequency Bioelectrical Impedance Analysis and Dual-Energy X-Ray Absorptiometry for Body Composition Assessment in Apparently Healthy Greek Adults.

Healthcare (Basel, Switzerland)·2026

Same journal

'It's Not About the Food'-Understanding the Lived Experience of Patients Who Developed Hospital-Acquired Malnutrition (HAM) and That of Their Carers.

Healthcare (Basel, Switzerland)·2026

Same journal

Unveiling the Humanizing and Therapeutic Values of Live Music in Healthcare Settings: A Scoping Review.

Healthcare (Basel, Switzerland)·2026

Same journal

Respiratory Rehabilitation and Decannulation in Adults with Prolonged Mechanical Ventilation After Tracheostomy: A Narrative Review.

Healthcare (Basel, Switzerland)·2026

See all related articles

This study introduces a novel method combining Mask R-CNN and YOLOv4 for accurate facial symptom detection, improving upon existing techniques for identifying conditions like acne and wrinkles.

Area of Science:

Computer Vision
Medical Image Analysis
Artificial Intelligence

Background:

Accurate facial symptom detection is challenging due to complex backgrounds and image noise.
Existing methods struggle with variations in facial expressions and interference from other faces.

Purpose of the Study:

To propose a novel hybrid method for precise facial symptom identification.
To enhance the accuracy of detecting dermatological conditions from facial images.

Main Methods:

A hybrid approach combining Mask Region-based Convolutional Neural Network (Mask R-CNN) for segmentation and You Only Look Once version 4 (YOLOv4) for detection.
Utilized ResNet-101 and Feature Pyramid Networks (FPN) for feature fusion and noise reduction.
Trained the model on public datasets like DermNet and Freepic.

Keywords:

Mask R-CNN YOLOv3 YOLOv4 facial symptom detection skin condition

More Related Videos

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Aug 16, 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

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Main Results:

The proposed method achieved mean average precision (mAP) scores of 57.73%, 60.38%, and 59.75% across different data amounts.
Demonstrated a performance improvement of over 3% in mAP compared to other methods.
Effectively identified facial symptoms including acne, freckles, and wrinkles.

Conclusions:

The combined Mask R-CNN and YOLOv4 approach offers effective and accurate facial symptom identification.
This method shows promise for dermatological applications, even with limited training data.
The technique successfully overcomes challenges posed by complex image backgrounds and noise.