TY - GEN
T1 - A deep-structural medical image classification for a Radon-based image retrieval
AU - Khatami, Amin
AU - Babaie, Morteza
AU - Khosravi, Abbas
AU - Tizhoosh, H. R.
AU - Salaken, Syed Moshfeq
AU - Nahavandi, Saeid
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/12
Y1 - 2017/6/12
N2 - Content-based image retrieval is an effective and efficient technique to retrieve images from a big dataset with similar images. To have a robust retrieval system, a proper and accurate classification scheme is required to categorise the information of shape, texture, and colours. In this paper, a deep convolutional neural network is proposed to classify the information of radiology images. Deep networks need millions of data, but the lack of availability of balanced large datasets in medical domain motivates us to trust on even the second prediction category rather than just the best one. Hence the best predicted categories are considered for a query test, followed by a similarity-based search technique. This results in a proper classification performance. Moreover, as Radon transformation is famous in medical domain, this conversion technique is utilized for a similarity-based search scheme, after measuring by a k-nearest neighbours algorithm. The experimental results and comparison show that this strategy not only improve the performance, but also save the computational costs.
AB - Content-based image retrieval is an effective and efficient technique to retrieve images from a big dataset with similar images. To have a robust retrieval system, a proper and accurate classification scheme is required to categorise the information of shape, texture, and colours. In this paper, a deep convolutional neural network is proposed to classify the information of radiology images. Deep networks need millions of data, but the lack of availability of balanced large datasets in medical domain motivates us to trust on even the second prediction category rather than just the best one. Hence the best predicted categories are considered for a query test, followed by a similarity-based search technique. This results in a proper classification performance. Moreover, as Radon transformation is famous in medical domain, this conversion technique is utilized for a similarity-based search scheme, after measuring by a k-nearest neighbours algorithm. The experimental results and comparison show that this strategy not only improve the performance, but also save the computational costs.
UR - http://www.scopus.com/inward/record.url?scp=85021803025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021803025&partnerID=8YFLogxK
U2 - 10.1109/CCECE.2017.7946756
DO - 10.1109/CCECE.2017.7946756
M3 - Conference contribution
AN - SCOPUS:85021803025
T3 - Canadian Conference on Electrical and Computer Engineering
BT - 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017
Y2 - 30 April 2017 through 3 May 2017
ER -