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dc.contributor.authorZhang, Yu-Dong
dc.contributor.authorGorriz Sáez, Juan Manuel 
dc.date.accessioned2021-01-20T11:32:03Z
dc.date.available2021-01-20T11:32:03Z
dc.date.issued2020-11
dc.identifier.citationZhang, Y. D., Satapathy, S. C., Wu, D., Guttery, D. S., Górriz, J. M., & Wang, S. H. (2020). Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling. Complex & Intelligent Systems, 1-16. [https://doi.org/10.1007/s40747-020-00218-4]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/65582
dc.description.abstractDuctal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.es_ES
dc.description.sponsorshipBritish Heart Foundation Accelerator Award, UKes_ES
dc.description.sponsorshipRoyal Society International Exchanges Cost Share Award, UK RP202G0230es_ES
dc.description.sponsorshipHope Foundation for Cancer Research, UK RM60G0680es_ES
dc.description.sponsorshipMedical Research Council Confidence in Concept Award, UK MC_PC_17171es_ES
dc.description.sponsorshipMINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR18es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDuctal carcinoma in situes_ES
dc.subjectThermal imageses_ES
dc.subjectDeep learninges_ES
dc.subjectConvolutional neural networkses_ES
dc.subjectBreast thermographyes_ES
dc.subjectExponential linear unites_ES
dc.subjectRank-based weighted poolinges_ES
dc.subjectData augmentationes_ES
dc.subjectColor jitteringes_ES
dc.subjectVisual question answeringes_ES
dc.titleImproving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted poolinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s40747-020-00218-4
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License