TY  - JOUR
Y1  - 2016///
VL  - 387
A1  - Khan, S.A.
A1  - Yong, S.-P.
JF  - Lecture Notes in Electrical Engineering
UR  - 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
ID  - scholars7878
KW  - Artificial intelligence; Computer aided diagnosis; Diagnosis; Learning systems; Medical imaging; Object recognition; Signal processing
KW  -  Anatomical structures; Anatomy classifications; Automatic classification; Computer-aided detection and diagnosis; Deep learning; Natural images
KW  -  Image classification
N2  - 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 â??Deep learningâ?? methods that are outperforming the contemporary methods in classification of images. Till now these â??Deep learningâ?? 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.
PB  - Springer Verlag
SN  - 18761100
EP  - 303
AV  - none
TI  - An evaluation of convolutional neural nets for medical image anatomy classification
SP  - 293
N1  - cited By 8; Conference of International Conference on Machine Learning and Signal Processing, MALSIP 2015 ; Conference Date: 12 June 2015 Through 14 June 2015; Conference Code:176019
ER  -