%0 Conference Paper
%A Khan, S.
%A Yong, S.-P.
%D 2017
%F scholars:8525
%I Institute of Electrical and Electronics Engineers Inc.
%K Classification (of information); Convolution; Deep learning; Image representation; Medical imaging; Network architecture; Neural networks, Comparative evaluations; Convolutional neural network; Images classification; Learning architectures; Medical anatomy; Medical domains; Natural images; Neural network architecture; Training data, Image classification
%P 1661-1668
%R 10.1109/APSIPA.2017.8282299
%T A deep learning architecture for classifying medical images of anatomy object
%U https://khub.utp.edu.my/scholars/8525/
%V 2018-F
%X Deep learning architectures particularly Convolutional Neural Network (CNN) have shown an intrinsic ability to automatically extract the high level representations from big data. CNN has produced impressive results in natural image classification, but there is a major hurdle to their deployment in medical domain because of the relatively lack of training data as compared to general imaging benchmarks such as ImageNet. In this paper we present a comparative evaluation of the three milestone architectures i.e. LeNet, AlexNet and GoogLeNet and propose our CNN architecture for classifying medical anatomy images. Based on the experiments, it is shown that the proposed Convolutional Neural Network architecture outperforms the three milestone architectures in classifying medical images of anatomy object. © 2017 IEEE.
%Z cited By 38; Conference of 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 ; Conference Date: 12 December 2017 Through 15 December 2017; Conference Code:134570