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A radon-based convolutional neural network for medical image retrieval
journal contribution
posted on 2018-06-01, 00:00 authored by A Khatami, M Babaie, H R Tizhoosh, Asef NazariAsef Nazari, Abbas KhosraviAbbas Khosravi, Saeid NahavandiImage classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known technology in medical field, is utilized along with a deep network to propose a retrieval system for a highly imbalanced medical benchmark. The main contribution of this study is to propose a deep model which is trained on the Radon-based transformed input data. The experimental results show that applying this transformation as input to feed into a convolutional neural network, significantly increases the performance, compared with other retrieval systems. The proposed scheme clearly increases the retrieval performance, compared with almost all models which use Radon transformation to retrieve medical images.
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Journal
International journal of engineeringVolume
31Issue
6Pagination
910 - 915Publisher
Materials and Energy Research Center (M E R C)Location
Tehran, IranPublisher DOI
ISSN
2423-7167eISSN
1735-9244Language
persianPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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