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Skin Cancer01:30

Skin Cancer

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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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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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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
PubMed
Summary

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.

Keywords:
Mask R-CNNYOLOv3YOLOv4facial symptom detectionskin condition

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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.

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.