A novel multistage CAD system for breast cancer diagnosis

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dc.authoridTunga, Burcu/0000-0001-7318-964X
dc.authoridUyar, Tevfik/0000-0003-0124-6910
dc.authorwosidTunga, Burcu/N-9314-2013
dc.contributor.authorKaracan, Kubra
dc.contributor.authorUyar, Tevfik
dc.contributor.authorTunga, Burcu
dc.contributor.authorTunga, M. Alper
dc.date.accessioned2024-05-19T14:39:36Z
dc.date.available2024-05-19T14:39:36Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractComputer-aided diagnosis (CAD) systems are widely used to diagnose breast cancer using mammography screening. In this research, we proposed a new multistage CAD system based on image decomposition with High-Dimensional Model Representation (HDMR) which is a divide-and-conquer algorithm. We used digital mammograms from Digital Database for Screening Mammography as dataset. We neglected BIRADS classification and used a brand-new clustering based on HDMR constant and breast size. To find the best performance of HDMR-based CAD system, we compared different pre-processing settings such as contrast enhancement with CLAHE and HDMR, feature extraction with HDMR, feature scaling, dimension reduction with Linear Discriminant Analysis. We used several Machine Learning algorithms and measured the performance of proposed system for normal-benign-malign classification, cancer detection, mass detection and found that the proposed system achieves 66%, 71% and 87% accuracy, respectively. We were able to achieve 92% accuracy, 100% sensitivity and 91% specificity in specific clusters. These results are comparable with deep learning-based methods although we simplified the pipeline and used brand-new HDMR-based processes.en_US
dc.identifier.doi10.1007/s11760-022-02453-3
dc.identifier.endpage2368en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85148376838en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2359en_US
dc.identifier.urihttps://doi.org10.1007/s11760-022-02453-3
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4812
dc.identifier.volume17en_US
dc.identifier.wosWOS:000936186600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBreast Cancer Diagnosisen_US
dc.subjectComputer-Aided Diagnosisen_US
dc.subjectImage Decompositionen_US
dc.subjectImage Clusteringen_US
dc.subjectContrast Enhancementen_US
dc.subjectHdmren_US
dc.titleA novel multistage CAD system for breast cancer diagnosisen_US
dc.typeArticleen_US

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