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HARIRAYA: a novel breast cancer pseudo-color feature for multimodal mammogram using deep learning


Citation

Ahmad Nazri, Azree Shahrel and Agbolade, Olalekan (2018) HARIRAYA: a novel breast cancer pseudo-color feature for multimodal mammogram using deep learning. International Journal of Engineering and Technology(UAE), 7 (4.31). 322 - 325. ISSN 2227-524X

Abstract

Breast cancer is the leading cancer in the world. Mammogram is a gold standard for detecting breast cancer at earlier screening because of its sensitivity. Standard grayscale mammogram images are used by expert radiologists and Computer Aided-Diagnosis (CAD) systems. Yet, this original x-ray color provides little information to human radiologists and CAD systems to make decision. This binary color code thus affects sensitivity and specificity of prediction and subsequently affects accuracy. In order to enhance classifier models’ perfor-mance, this paper proposes a novel feature-level data integration method that combines features from grayscale mammogram and spec- trum mammogram based on a deep neural network (DNN), called HARIRAYA. Pseudo-color is generated using spectrum color code to produce Spectrum mammogram from grayscale mammogram. The DNN is trained with three layers: grayscale, false-color and joint fea-ture representation layers. Empirical results show that the multi-modal DNN model has a better performance in the prediction of malig- nant breast tissue than single-modal DNN using HARIRAYA features.


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Additional Metadata

Item Type: Article
Divisions: Institute of Bioscience
DOI Number: https://doi.org/10.14419/ijet.v7i4.31.23389
Publisher: Science Publishing Corporation
Keywords: Breast Cancer; Convolutional neural network; Mammogram; Multi-modal features; False color
Depositing User: Mr. Sazali Mohamad
Date Deposited: 17 Oct 2020 20:34
Last Modified: 17 Oct 2020 20:34
Altmetrics: https://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.14419/ijet.v7i4.31.23389
URI: http://psasir.upm.edu.my/id/eprint/74491
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