A deep-structural medical image classification for a Radon-based image retrieval

Amin Khatami, Morteza Babaie, Abbas Khosravi, H. R. Tizhoosh, Syed Moshfeq Salaken, Saeid Nahavandi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509055388
DOIs
StatePublished - Jun 12 2017
Event30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017 - Windsor, Canada
Duration: Apr 30 2017May 3 2017

Publication series

NameCanadian Conference on Electrical and Computer Engineering
ISSN (Print)0840-7789

Conference

Conference30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017
Country/TerritoryCanada
CityWindsor
Period4/30/175/3/17

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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