Automatic Diagnosis of the Footprint Pathologies Based on Neural Networks

  • Marco Mora
  • Mary Carmen Jarur
  • Daniel Sbarbaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)


Currently foot pathologies, like cave and flat foot, are detected by an human expert who interprets a footprint image. The lack of trained personal to carry out massive first screening detection campaigns precludes the routinary diagnostic of these pathologies. This work presents a novel automatic system, based on Neural Networks (NN), for foot pathologies detection. In order to improve the efficiency of the neural network training algorithm, we propose the use of principal components analysis to reduce the number of inputs to the NN. The results obtained with this system demonstrate the feasibility of building automatic diagnosis systems based on the foot image. These systems are very valuable in remote areas and can be also used for massive first screening health campaigns.


Neural Network Hide Layer Human Expert Automatic Diagnosis Screening Detection 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Marco Mora
    • 1
  • Mary Carmen Jarur
    • 1
  • Daniel Sbarbaro
    • 2
  1. 1.Department of Computer Science, Catholic University of Maule, Casilla 617, TalcaChile
  2. 2.Department of Electrical Engineering, University of Concepcion, Casilla 160-C, ConcepcionChile

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