Model of images of structureless areas for the analysis of pigment patterns using artificial intelligence in oncodermatology.

This study addresses the creation of a new model for structureless areas of pigmented skin neoplasms intended for use in hardware and software systems for pattern recognition in oncodermatology. The problem which development of the model seeks to solve is the difficult formalization of the metaphori...

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Publicado en:Biomedical Engineering Vol. 59; no. 2; pp. 78 - 83
Autores principales: Nikitaev, V. G., Sergeev, V. Yu., Kegelik, N. A., Kozlov, V. S.
Formato: pictorial research tables/charts Journal Article
Publicado: Springer Nature Jul2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2025
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10527-025-10467-7
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        atl: Model of images of structureless areas for the analysis of pigment patterns using artificial intelligence in oncodermatology.
      aug:
        au:
          Nikitaev, V. G.
          Sergeev, V. Yu.
          Kegelik, N. A.
          Kozlov, V. S.
        affil: https://ror.org/04w8z7f34 Department of Computerized Medical Systems, National Research Nuclear University MEPhI, Moscow, Russian Federation
      sug:
        subj:
          Pigments
          Artificial Intelligence Utilization
          Oncology
          Skin Neoplasms Diagnosis
          Dermoscopy
          Sensitivity and Specificity
          Prediction Models
          Human
          Experimental Studies
          Skin Pigmentation
          Descriptive Statistics
      ab: This study addresses the creation of a new model for structureless areas of pigmented skin neoplasms intended for use in hardware and software systems for pattern recognition in oncodermatology. The problem which development of the model seeks to solve is the difficult formalization of the metaphorical medical definition of this element of pigment patterns. The proposed model of structureless areas is based on analysis of the texture characteristics of dermatoscopic images. The study used a set of 400 images containing a variety of dermatoscopic structures and structureless areas. An experimental study demonstrated the adequacy of the proposed model, which supports discrimination between structureless areas and other structures in dermatoscopic images with accuracy of up to 92.2%.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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