Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines

 
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2005 (EN)
Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines (EN)

Γκλώτσος, Δημήτριος (EL)
Κάβουρας, Διονύσης Α. (EL)
Ραβαζούλα, Παναγιώτα (EL)
Νικηφορίδης, Γεώργιος Χ. (EL)
Tohka, Jussi (EN)

Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε. (EL)

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. (EN)

journalArticle

Probabilistic neural network (EN)
Microscopy (EN)
Πιθανοτικό νευρωνικό δίκτυο (EN)
Μικροσκοπία (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

International Journal of Neural Systems (EN)

English

2005

DOI: 10.1142/S0129065705000013

World Scientific Publishing (EN)



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