%T An evaluation of convolutional neural nets for medical image anatomy classification
%V 387
%I Springer Verlag
%A S.A. Khan
%A S.-P. Yong
%P 293-303
%K Artificial intelligence; Computer aided diagnosis; Diagnosis; Learning systems; Medical imaging; Object recognition; Signal processing, Anatomical structures; Anatomy classifications; Automatic classification; Computer-aided detection and diagnosis; Deep learning; Natural images, Image classification
%X 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.
%D 2016
%R 10.1007/978-3-319-32213-1₂₆
%O 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
%J Lecture Notes in Electrical Engineering
%L scholars7878