IJE TRANSACTIONS C: Aspects Vol. 31, No. 6 (June 2018) 1257-1265   

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A. Khatami, M. Babaie, Hamid R. Tizhoosh, A. Khosravi and S. Nahavandi
( Received: August 22, 2017 – Accepted: March 09, 2018 )

Abstract    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.


Keywords    deep CNN, medical image retrieval, Radon transformation,IRMA


چکیده    اخیرا سیستم های دسته بندی و بازیابی تصاویر پزشکی به دلیل دسترسسی به موارد مشابه ذخیره شده و پیگیری خط سیر درمان این موارد مشابه، بیشتر از هر زمانی مورد توجه قرار گرفته اند. با این حال، به علت عدم وجود داده های متوازن و یرچسبگذاری شده در ابعاد وسیع (به خصوص در تصاویر پزشکی) دسترسی به این سیستم ها را دشوار ساخته است. در این مطالعه به منظور ایجاد یک سیستم بازیابی در یک دیتابیس غیرمتوازن، از تبدیل Radon تصاویر به همراه یک شبکه عصبی عمیق استفاده گردیده است. در بخش نتایج نشان داده خواهد شد که اعمال این تبدیل به لایه ورودی یک شبکه عمیق، دقت بازیابی اطلاعات را به طور چشمگیری افزایش می دهد.

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