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Skin Disease Classification using Neural Network.

Usama Ijaz Bajwa1, Sardar Alam2, Nuhman Ul Haq2

  • 1Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.

Current Medical Imaging
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated skin disease classification system using Artificial Neural Networks (ANNs) and statistical feature extraction. The novel approach aids physicians in detecting and classifying skin lesions accurately.

Keywords:
Skin diseaseUltraviolet (UV) raysartificial neural networkclassificationlesionmedical abnormalities

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Area of Science:

  • Dermatology
  • Computer Science
  • Medical Imaging

Background:

  • Automated skin disease classification is crucial for early diagnosis and treatment.
  • Existing methods may lack accuracy or require manual intervention.
  • Developing an efficient, automated system can support general physicians in clinical practice.

Purpose of the Study:

  • To develop a fully automatic system for skin disease classification.
  • To assist general physicians in detecting and classifying skin lesions.
  • To improve diagnostic accuracy and efficiency in dermatology.

Main Methods:

  • Utilized statistical feature extraction, including first and second-order statistical moments.
  • Employed entropy of different color channels and texture-based features for classification.
  • Implemented an Artificial Neural Network (ANN) for automated classification of skin lesions.

Main Results:

  • The proposed approach demonstrated effectiveness in constructing a skin disease classification system.
  • Extensive experiments validated the performance on a dataset of 588 images with 6907 lesion regions.
  • The system achieved reliable classification of skin lesions.

Conclusions:

  • The developed methodology is effective for building a robust skin disease classification system.
  • Further research is required to assess performance across diverse skin tones.
  • Adaptation for various skin tones will enhance the system's clinical applicability.