Name
Abstract | ||
|---|---|---|
Some foot plant pathologies, like cave and flat foot, are normally detected by a human expert by means of footprint images. Nevertheless, the lack of trained personal to accomplish such massive first screening detection efforts precludes the routinely diagnostic of the above mentioned pathologies. In this work an innovative automatic system for foot plant pathologies based on neural networks (NN) is presented. We propose the use of principal components analysis to reduce the number of inputs to the NN and therefore increasing the efficiency of the training algorithm. The results achieved with this system evidence the feasibility of establishing automatic diagnosis systems based on the footprint image. These systems are of a great value specially in apart areas and are also suited to carry on massive first screening health campaigns. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72393-6_136 | ISNN (2) |
Keywords | Field | DocType |
flat foot,system evidence,automatic diagnosis,great value,footprint image,neural networks approach,innovative automatic system,screening detection effort,foot plant,foot plant pathology,screening health campaign,automatic diagnosis system,foot plant pathologies,principal component analysis,neural network | Computer science,Artificial intelligence,Footprint,Artificial neural network,Principal component analysis,Machine learning | Conference |
Volume | ISSN | Citations |
4492 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Mora | 1 | 32 | 9.08 |
| Mary Carmen Jarur | 2 | 4 | 1.82 |
| Daniel Sbarbaro | 3 | 49 | 12.84 |
| Leopoldo Pavesi | 4 | 3 | 1.40 |