Abbas Khosravi - Deakin University, Institute for Intelligent System Research and Inno
Hamid R. Tizhoosh - University of Waterloo, KIMIA Lab, Canada
Morteza Babaie - University of Waterloo, KIMIA Lab, Canada
Amin Khatami - Deakin University, Institute for Intelligent System Research and Innovation

Image 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.

Deep convolutional neural network, Image Retrieval in Medical Application, Medical Image Retrieval, Radon Transformation