Computer-Aided Diagnosis in Wound Images with Neural Networks

  • María Navas
  • Rafael M. Luque-Baena
  • Laura Morente
  • David Coronado
  • Rafael Rodríguez
  • Francisco J. Veredas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)


Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to success on the treatment decision and, in some cases, to save the patient’s life. However, current evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detecting and classifying wound tissue types which play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and a standard multilayer perceptron neural network to classify effectively each segmented region as the appropriate tissue type. Results obtained show a high performance rate which enables to support ulcer diagnosis by a reliable computational system.


Support Vector Machine Colour Space Pressure Ulcer Healing Tissue Tissue Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • María Navas
    • 1
  • Rafael M. Luque-Baena
    • 1
  • Laura Morente
    • 2
  • David Coronado
    • 3
  • Rafael Rodríguez
    • 3
  • Francisco J. Veredas
    • 1
  1. 1.Dpto. Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain
  2. 2.Escuela Universitaria de Enfermería, Diputación Provincial de MálagaMálagaSpain
  3. 3.Wimasis SLCórdobaSpain

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