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Related Concept Videos

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Deep learning based classification of facial dermatological disorders.

Evgin Goceri1

  • 1Department of Biomedical Engineering, Engineering Faculty, Akdeniz University, Turkey.

Computers in Biology and Medicine
|November 22, 2020
PubMed
Summary

This study introduces an automated method for classifying dermatological diseases from images. The approach achieves high accuracy (95.24%) in detecting and classifying skin lesions using deep learning and a novel loss function.

Keywords:
Active contoursAutomated diagnosisDenseNet201Lesion segmentationLoss functionSkin disease

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Dermatological diseases often present with visible skin lesions, making them suitable for image-based diagnosis.
  • Automated diagnostic tools offer objective, timely, and accessible dermatological assessments, potentially reducing healthcare costs and patient wait times.

Purpose of the Study:

  • To develop and evaluate an automated method for classifying common facial dermatological diseases from digital photographs.
  • To improve the accuracy and efficiency of dermatological diagnosis through advanced image analysis and deep learning techniques.

Main Methods:

  • A two-stage approach involving automated lesion detection and segmentation using variational level set techniques, followed by classification using a pre-trained DenseNet201 architecture with a novel loss function.
  • The study also includes a comprehensive survey of deep learning methods for dermatological disease classification and comparative evaluations of ten convolutional neural networks.

Main Results:

  • The proposed automated method achieved a high classification accuracy of 95.24% for five common facial dermatological diseases.
  • The developed lesion detection and segmentation technique based on level sets, combined with the efficient loss function, significantly contributed to the high performance.

Conclusions:

  • The automated system demonstrates significant potential for accurate and efficient dermatological disease classification from images.
  • This technology can aid in early diagnosis, reduce the burden on dermatologists, and improve patient outcomes for conditions causing significant psychological distress.