Automatic Diagnosis of the Footprint Pathologies Based on Neural Networks

Authors:
Marco Mora;Mary Carmen Jarur;Daniel Sbarbaro
Affiliations:
Department of Computer Science, Catholic University of Maule, Casilla 617, Talca, Chile;Department of Computer Science, Catholic University of Maule, Casilla 617, Talca, Chile;Department of Electrical Engineering, University of Concepcion, Casilla 160-C, Concepcion, Chile
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

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.