relation: https://khub.utp.edu.my/scholars/8525/
title: A deep learning architecture for classifying medical images of anatomy object
creator: Khan, S.
creator: Yong, S.-P.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Khan, S. and Yong, S.-P.  (2017) A deep learning architecture for classifying medical images of anatomy object.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050496390&doi=10.1109%2fAPSIPA.2017.8282299&partnerID=40&md5=e62632eca7df715c1261ea08d17b6dfe
relation: 10.1109/APSIPA.2017.8282299
identifier: 10.1109/APSIPA.2017.8282299