Name
Title | ||
|---|---|---|
Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms |
Abstract | ||
|---|---|---|
Misleading diagnosis of skin diseases may result in complications during the healing process. Skin images provide an important contribution to medical staff on storing and exchanging information to try preventing misdiagnosis. For such, image segmentation process may benefit from use of machine learning techniques, increasing simplicity of procedure, reducing computational costs and improving the diagnosis. This paper presents a comparison among different paradigms of machine learning to validate the segmentation of medical images of lower members ulcers, this segmentation allows wound pattern recognition to determinate injury region aiming at reducing the subjectivity of human evaluation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CBMS.2015.48 | IEEE Symposium on Computer-Based Medical Systems |
Keywords | Field | DocType |
Image segmentation, Machine learning classifiers, Medical images, Pattern recognition | Computer vision,Pattern recognition,Segmentation,Computer science,Image segmentation,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2372-9198 | 2 | 0.39 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Luis Seixas | 1 | 2 | 0.39 |
| Sylvio Barbon | 2 | 46 | 10.97 |
| Rafael Gomes Mantovani | 3 | 28 | 5.12 |