relation: https://khub.utp.edu.my/scholars/7878/
title: An evaluation of convolutional neural nets for medical image anatomy classification
creator: Khan, S.A.
creator: Yong, S.-P.
description: Classification of the anatomical structures is an important precondition for several computer aided detection and diagnosis systems. Attaining extraordinary precision for automatic classification is a stimulating job because of vast amount of variation in the anatomical structures. Current trend in object recognition is driven by �Deep learning� methods that are outperforming the contemporary methods in classification of images. Till now these �Deep learning� methods have been applied on natural images. In this study, we compare the performance of three main Deep learning architectures i.e. LeNet, AlexNet, GoogLeNet on medical imaging data containing five anatomical structures for anatomic specific classification. © Springer International Publishing Switzerland 2016.
publisher: Springer Verlag
date: 2016
type: Article
type: PeerReviewed
identifier:   Khan, S.A. and Yong, S.-P.  (2016) An evaluation of convolutional neural nets for medical image anatomy classification.  Lecture Notes in Electrical Engineering, 387.  pp. 293-303.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975889824&doi=10.1007%2f978-3-319-32213-1_26&partnerID=40&md5=e0aee8136e3f43b73b90d966fa82d687
relation: 10.1007/978-3-319-32213-1₂₆
identifier: 10.1007/978-3-319-32213-1₂₆