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Skin Disease Recognition Method Based on Image Color and Texture Features.

Li-Sheng Wei1, Quan Gan1, Tao Ji1

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This study introduces an automated method for identifying herpes, dermatitis, and psoriasis skin diseases using image analysis. The new approach enhances diagnostic accuracy for multiple skin conditions.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Skin diseases significantly impact patient health and quality of life.
  • Accurate diagnosis of multitype skin diseases requires advanced methods.
  • Current single-disease identification approaches lack efficiency for complex cases.

Purpose of the Study:

  • To develop and validate a novel automated method for identifying three common skin diseases: herpes, dermatitis, and psoriasis.
  • To improve the accuracy and efficiency of skin disease diagnosis through image analysis.
  • To establish a feasible computational approach for differentiating between specific skin pathologies.

Main Methods:

  • Skin images were preprocessed using filtering and transformation techniques to remove noise and background.
  • Image segmentation was performed using the grey-level co-occurrence matrix (GLCM) method.
  • Texture and color features were extracted, and a support vector machine (SVM) classifier was employed for disease identification.

Main Results:

  • The proposed method accurately extracted texture and color features from skin disease images.
  • The support vector machine (SVM) successfully identified three types of skin diseases: herpes, dermatitis, and psoriasis.
  • Experimental results confirmed the effectiveness and feasibility of the developed recognition method.

Conclusions:

  • The automated image analysis method demonstrates high potential for accurate skin disease diagnosis.
  • This approach offers a viable solution for the accurate identification of multiple skin conditions.
  • Further research can expand this method for diagnosing a wider range of dermatological disorders.