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Breast Cancer Diagnosis in Dce-Mri Using Mixture Ensemble of Convolutional Neural Networks Publisher



Rasti R1, 2 ; Teshnehlab M3 ; Phung SL4
Authors
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Authors Affiliations
  1. 1. Artificial Intelligent Lab, Department of Electrical Engineering of K. N. Toosi University of Technology, Tehran, Iran
  2. 2. Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Electrical Engineering of K. N. Toosi University of Technology, Tehran, Iran
  4. 4. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia

Source: Pattern Recognition Published:2017


Abstract

This work addresses a novel computer-aided diagnosis (CAD) system in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed based on a mixture ensemble of convolutional neural networks (ME-CNN) to discriminate between benign and malignant breast tumors. The ME-CNN is a modular and image-based ensemble, which can stochastically partition the high-dimensional image space through simultaneous and competitive learning of its modules. The proposed system was assessed on our database of 112 DCE-MRI studies including solid breast masses, using a wide range of classification measures. The ME-CNN model composed of three CNN experts and one convolutional gating network achieves an accuracy of 96.39%, a sensitivity of 97.73% and a specificity of 94.87%. The experimental results also show that it has competitive classification performances compared to three existing single-classifier methods and two convolutional ensemble methods. The proposed ME-CNN model could provide an effective tool for radiologists to analyse breast DCE-MRI images. © 2017
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