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|Title:||Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines|
Κάβουρας, Διονύσης Α.
Νικηφορίδης, Γεώργιος Χ.
|Item type:||Journal article|
|Keywords:||Probabilistic neural network;Microscopy;Πιθανοτικό νευρωνικό δίκτυο;Μικροσκοπία|
|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.|
|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)|
|License:||Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες|
|Appears in Collections:||Δημοσιεύσεις|
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