Name
Abstract | ||
|---|---|---|
Locating regions of tumor in digital mammography images is a hard task even for experts. Consequently, due to medical experience, different diagnoses to an image are commonly found. Therefore, the use of an automatic approach for detecting tumor regions is important to avoid misdiagnosis. In this work, the Extreme Learning Machine (ELM) neural network was used to segment tumor regions of digitized mammograms available in the Mini-Mias database. A set of images were selected for training, while different images were used for testing. Results showed that ELM provides an over 81% classification rate, being able to segment the region of tumor with high accuracy. By comparing ELM with MLP network, it was possible to conclude that ELM has a faster learning time, with a higher training and testing accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32639-4_12 | IDEAL |
Keywords | Field | DocType |
neural network,tumor detection,different diagnosis,mlp network,higher training,extreme learning machine,segment tumor region,testing accuracy,tumor region,different image,high accuracy | Digital mammography,Mammography,Pattern recognition,Segmentation,Computer science,Extreme learning machine,Artificial intelligence,Artificial neural network,Classification rate,Medical diagnosis,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.38 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. R. Cordeiro | 1 | 46 | 7.14 |
| Sidney M. L. Lima | 2 | 15 | 2.25 |
| Abel Guilhermino Silva-Filho | 3 | 62 | 12.94 |
| Santos W.P. | 4 | 2 | 0.38 |