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A deep-structural medical image classification for a Radon-based image retrieval
conference contribution
posted on 2017-06-12, 00:00 authored by Seyedamin Khatami, M Babaie, Abbas KhosraviAbbas Khosravi, H R Tizhoosh, Syed Moshfeq Salaken, Saeid Nahavandi© 2017 IEEE. 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.
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Event
Electrical and Computer Engineering. IEEE Canadian Conference (30th : 2017 : Windsor, Ontario)Pagination
1 - 4Publisher
IEEELocation
Windsor, OntarioPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2017-04-30End date
2017-05-03ISSN
0840-7789ISBN-13
9781509055388Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2017, IEEETitle of proceedings
CCECE 2017 : Proceedings of the IEEE 30th Canadian Conference on Electrical and Computer EngineeringUsage metrics
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