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Training a learning vector quantization network for biomedical classification

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dc.contributor.author Anagnostopoulos, C en
dc.contributor.author Anagnostopoulos, J en
dc.contributor.author Vergados, DD en
dc.contributor.author Kayafas, E en
dc.contributor.author Loumos, V en
dc.contributor.author Theodoropoulos, G en
dc.date.accessioned 2014-03-01T02:42:02Z
dc.date.available 2014-03-01T02:42:02Z
dc.date.issued 2001 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30738
dc.subject Image processing en
dc.subject Learning vector quantization en
dc.subject Neural networks en
dc.subject Parasite identification en
dc.subject.other Biomedical engineering en
dc.subject.other Feature extraction en
dc.subject.other Image analysis en
dc.subject.other Learning systems en
dc.subject.other Medical imaging en
dc.subject.other Object recognition en
dc.subject.other Relational database systems en
dc.subject.other Vector quantization en
dc.subject.other Biomedical classification en
dc.subject.other Learning vector quantization en
dc.subject.other Parasite identification en
dc.subject.other Neural networks en
dc.title Training a learning vector quantization network for biomedical classification en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IJCNN.2001.938761 en
heal.identifier.secondary http://dx.doi.org/10.1109/IJCNN.2001.938761 en
heal.publicationDate 2001 en
heal.abstract A competitive Learning Vector Quantization (LVQ) Artificial Neural Network (ANN) was trained to identify third stage parasitic strongyle larvae from domestic animals on the basis of quantitative data obtained from processed digital images of larvae. For this reason various novel quantitative features obtained from processed digital images of larvae were tested whether they are variant or invariant to the shape taken by the motile larvae during image recording. A total of 255 images of 57 individual larvae in various shapes belonging to 5 genera were recorded. Following image processing 16 novel features were measured of which 7 were selected as invariant to larva shape. By trial and error two of those novel features ""area"" and ""perimeter"" along with the quantitative features used in conventional identification, ""overall body length"", ""width"" and ""tail of sheath"" were used as an effective training data set for the ANN. This ANN coupled with an image analysis facility and a knowledge relational database became the basis for developing a computer-based larva identification system whose overall identification performance was 91.9%. The advantages of this system are its speed and objectivity. The objectivity of the system is based on the fact that it is not subject to inter- and intra-observer variability arising from the user's profile of competency in interpreting subjective and non-quantifiable descriptions. The limitations of the system are that it can not handle raw images but only data extracted from images, its performance depends on the reliability of the input vectors used as training data for the ANN and its use is restricted only in well equipped laboratories due to its requirement for expensive instrumentation. en
heal.journalName Proceedings of the International Joint Conference on Neural Networks en
dc.identifier.doi 10.1109/IJCNN.2001.938761 en
dc.identifier.volume 4 en
dc.identifier.spage 2506 en
dc.identifier.epage 2511 en


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