| dc.creator | Georgakopoulos, S. V. | en |
| dc.creator | Tasoulis, S. K. | en |
| dc.creator | Plagianakos, V. P. | en |
| dc.creator | Maglogiannis, I. | en |
| dc.date.accessioned | 2015-11-23T10:27:31Z | |
| dc.date.available | 2015-11-23T10:27:31Z | |
| dc.date.issued | 2013 | |
| dc.identifier | 10.1007/978-3-642-41013-0_30 | |
| dc.identifier.isbn | 9783642410123 | |
| dc.identifier.issn | 18650929 | |
| dc.identifier.uri | http://hdl.handle.net/11615/27728 | |
| dc.description.abstract | In this study we present a computer assisted image identification and recognition tool that aims to help the diagnosis of idiopathic pulmonary fibrosis in microscopy images. To this end, we use principal components analysis to reduce the dimensionality of the data and subsequently we perform classification using Artificial Neural Networks. The proposed approach succeeded in locating the pathological regions and achieved high quality results in terms of classification accuracy. © Springer-Verlag Berlin Heidelberg 2013. | en |
| dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84904662266&partnerID=40&md5=f8f23f662c8b631ede0122099b4f13ce | |
| dc.subject | Artificial Neural Network | en |
| dc.subject | Classification | en |
| dc.subject | Idiopathic Pulmonary Fibrosis | en |
| dc.subject | Pattern Recognition | en |
| dc.subject | Principal Components Analysis | en |
| dc.subject | Applications | en |
| dc.subject | Classification (of information) | en |
| dc.subject | Neural networks | en |
| dc.subject | Classification accuracy | en |
| dc.subject | Computer assisted | en |
| dc.subject | High quality | en |
| dc.subject | Image identification | en |
| dc.subject | Microscopy images | en |
| dc.subject | Principal component analysis | en |
| dc.title | Artificial Neural Networks and Principal Components Analysis for Detection of Idiopathic Pulmonary Fibrosis in Microscopy Images | en |
| dc.type | other | en |
