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Artificial Intelligence Based Skin Classification Using GMM.

M Monisha1, A Suresh2, M R Rashmi3

  • 1S.A. Engineering College, Anna University, Chennai, India.

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|November 22, 2018
PubMed
Summary

This study introduces an Artificial Neural Network (ANN) for diagnosing skin conditions like measles and chickenpox by analyzing texture and symptoms. This AI approach offers faster and more accurate disease recognition compared to traditional methods.

Keywords:
Dominant rotated local binary pattern (DRLBP)Gaussian mixture model classifier (GMM)Gray level co-occurrence matrix (GLCM)Pre-processingProbabilistic neural network (PNN) classificationSuper pixel segmentation

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Dermatology

Background:

  • Dermatological conditions such as measles, German measles, and chickenpox often present with similar symptoms like redness and rash.
  • Accurate diagnosis of these skin diseases can be challenging and time-consuming due to symptom overlap and the need to analyze extensive patient data.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnostic system utilizing Artificial Neural Networks (ANNs).
  • To improve the speed and accuracy of diagnosing skin conditions by analyzing visual patterns and symptoms.

Main Methods:

  • The study employed image processing techniques to extract relevant features from skin images.
  • An Artificial Neural Network (ANN) classifier was trained to recognize patterns associated with specific dermatological diseases.
  • The system processed image data, extracted characteristics, and categorized information for disease recognition.

Main Results:

  • The developed ANN system demonstrated the potential for faster prognosis and recognition of skin diseases compared to human diagnosis.
  • The system effectively analyzed symptom patterns for improved disease identification.

Conclusions:

  • Artificial Neural Networks offer a promising tool for computer-aided diagnosis in dermatology, potentially enhancing diagnostic efficiency.
  • This AI-driven approach can assist clinicians in making quicker and more accurate treatment decisions for skin conditions.