Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Feature for mammography Mass Classification

Azam Hamidinekoo, Zaineb Chelly Dagdia, Zobia Suhail, Reyer Zwiggelaar

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

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Abstract

—Breast cancer has a high incidence among women worldwide. This, together with the recent developments in deep learning based convolutional networks, have motivated research towards the enhancement of Computer Aided Diagnosis (CAD) systems. In this paper, the performance of a densely connected
convolutional network (DenseNet) for breast cancer was investigated for the malignant/benign classification of mammographic masses. Different mammography data sets were collected to investigate the capacity of this network for learning a combination of these databases. To achieve this, internal low-level, mid-level and high-level features/abstracts were extracted from
the model together with hand-crafted features, generating a vast amount of data. Using the distributed rough set based feature selection approach (Sp-RST), significant features were selected from both deep learning based features and hand-crafted ones, and fed into a learning model with separate and combined
data approaches for the classification of mammographic masses. Results show that by using Sp-RST as a powerful technique capable of performing big data preprocessing, DenseNet had the representational capacity to learn mammographic abnormalities
Original languageEnglish
Title of host publication2018 IEEE International Conference on BIG DATA
PublisherIEEE Press
Publication statusPublished - 2018
Event2018 IEEE International Conference on BIG DATA - The Westin Seattle, Seattle, United States of America
Duration: 10 Dec 201813 Dec 2018

Conference

Conference2018 IEEE International Conference on BIG DATA
Country/TerritoryUnited States of America
CitySeattle
Period10 Dec 201813 Dec 2018

Keywords

  • breast cancer
  • feature selection
  • DenseNet
  • Big Data
  • mass classification

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