Automatic Diagnosis of the Footprint Pathologies Based on Neural Networks
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.
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- 1.Foresee, D., Hagan, M.: Gauss-Newton Approximation to Bayesian Learning. In: Proceedings of the International Joint Conference on Neural Networks (1997)Google Scholar
- 2.Jollife, I.: Principal Component Analysis. Springer, Heidelberg (1986)Google Scholar
- 3.Mackay, D.: Bayesian Interpolation. Neural Computation 4(3) (1992)Google Scholar
- 5.Nguyen, D., Widrow, B.: Improving the Learning Speed of 2-Layer Neural Networks by Choossing Initial Values of the Adaptive Weights. In: Proceedings of the IJCNN, vol. 3, pp. 21–26 (1990)Google Scholar
- 6.Rumelhart, D., McClelland, J., PDP group: Explorations in Parallel Distributed Processing, vol. 1-2. MIT Press, Cambridge (1986)Google Scholar
- 7.Valenti, V.: Orthotic Treatment of Walk Alterations ( in spanish). Panamerican Medicine (1979)Google Scholar