Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/10115
Title: Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines
Authors: Γκλώτσος, Δημήτριος
Ραβαζούλα, Παναγιώτα
Κάβουρας, Διονύσης Α.
Νικηφορίδης, Γεώργιος Χ.
Tohka, Jussi
Item type: Journal article
Keywords: Probabilistic neural network;Microscopy;Πιθανοτικό νευρωνικό δίκτυο;Μικροσκοπία
Subjects: Medicine
Biomedical engineering
Ιατρική
Βιοϊατρική τεχνολογία
Issue Date: 11-May-2015
2005
Publisher: World Scientific Publishing
Abstract: A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.
Language: English
Citation: Glotsos, D., Tohka, J., Ravazoula, P., Cavouras, D. and Nikiforidis, G. (February & April 2005). Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines. International Journal of Neural Systems. 15(01n02). pp. 1-11. World Scientific Publishing: 2005.
Journal: International Journal of Neural Systems
Type of Journal: With a review process (peer review)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/10115
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