Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/5169
Title: Accelerating the design of probabilistic neural networks for computer aided diagnosis in mammography, employing graphics processing units
Authors: Σιδηρόπουλος, Κωνσταντίνος
Κάβουρας, Διονύσης Α.
Παγώνης, Νικόλαος
Δημητρόπουλος, Νικόλαος
Stonham, John T.
Item type: Conference publication
Keywords: Nuclear medicine;Πυρηνική ιατρική;Probabilistic neural networks;Graphics processing units;Πιθανοτικά νευρωνικά δίκτυα;Μονάδες επεξεργασίας γραφικών
Subjects: Medicine
Medical physics
Ιατρική
Ιατρική φυσική
Technology
Τεχνολογία
Issue Date: 30-Jan-2015
2010
Date of availability: 30-Jan-2015
Publisher: Νερατζής, Ηλίας
Σιανούδης, Ιωάννης
Βαλαής, Ιωάννης Γ.
Φούντος, Γεώργιος Π.
Abstract: The aim of this study is to propose a Probabilistic Neural Network (PNN) classifier system that can operate on a consumer-level graphics processing unit (GPU) and thus, harvest its tremendous parallel computation potential in order to accelerate the training phase. Therefore, the computationally intensive training of a PNN classifier system incorporating the exhaustive search of feature combinations and the leave-one-out techniques, was effectively ported on a medium class GPU device. Programming of the GPU was accomplished by means of the CUDA framework. The proposed system was tested on a real training dataset comprising 80 patterns, each consisting of 20 textural features extracted from digital mammograms (40 normal and 40 containing micro-calcifications) by an experienced physician. The developed GPU-based classifier was trained and the required time was measured. The latter was then compared with the respective training time of the same classifier running on a typical CPU and programmed in the C programming language. According to experimental results, the proposed GPU-based classifier achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 10 to 75 times.
Description: Special issue: Scientific papers presented on the 3nd International Conference on Experiments/Process/System Modeling/Simulation & Optimization in Athens, 8-11 July, 2009. Mini symposium on Medical Imaging, organized by G. Panayiotakis, I. Kandarakis, G. Fountos and I. Valais
Language: English
Citation: Sidiropoulos, K., Cavouras, D.A., Pagonis, N., Dimitropoulos, N. and Stonham, J.T. (2010). Accelerating the design of probabilistic neural networks for computer aided diagnosis in mammography, employing graphics processing units. "e-Journal of Science & Technology". [Online] 5(2): 49-54. Available from: http://e-jst.teiath.gr/
Journal: e-Περιοδικό Επιστήμης & Τεχνολογίας
e-Journal of Science & Technology
Type of Journal: With a review process (peer review)
Access scheme: Publicly accessible
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/5169
Appears in Collections:Τόμος 05, τεύχος 2 (2010)

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