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dc.contributor.author Hamidinekoo, Azam
dc.contributor.author Chelly Dagdia, Zaineb
dc.contributor.author Suhail, Zobia
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2020-02-24T03:10:27Z
dc.date.available 2020-02-24T03:10:27Z
dc.date.issued 2018
dc.identifier.citation Hamidinekoo , A , Chelly Dagdia , Z , Suhail , Z & Zwiggelaar , R 2018 , Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Feature for mammography Mass Classification . in 2018 IEEE International Conference on BIG DATA . IEEE Press , 2018 IEEE International Conference on BIG DATA , Seattle , United States of America , 10 Dec 2018 . en
dc.identifier.citation conference en
dc.identifier.other PURE: 29036371
dc.identifier.other PURE UUID: 2dea999b-0466-4cbe-bf67-954b76aeb5b7
dc.identifier.other handle.net: http://hdl.handle.net/2160/2dea999b-0466-4cbe-bf67-954b76aeb5b7
dc.identifier.uri http://hdl.handle.net/2160/47580
dc.description.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 en
dc.language.iso eng
dc.publisher IEEE Press
dc.relation.ispartof 2018 IEEE International Conference on BIG DATA en
dc.rights en
dc.subject breast cancer en
dc.subject feature selection en
dc.subject DenseNet en
dc.subject Big Data en
dc.subject mass classification en
dc.title Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Feature for mammography Mass Classification en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference en
dc.description.version authorsversion en
dc.contributor.institution Department of Computer Science en


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