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dc.contributor.author Hoon Yap, Moi
dc.contributor.author Goyal, Manu
dc.contributor.author Osman, Fatima
dc.contributor.author Martí, Robert
dc.contributor.author Denton, Erika
dc.contributor.author Juette, Arne
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2018-11-07T19:33:09Z
dc.date.available 2018-11-07T19:33:09Z
dc.date.issued 2018-10-10
dc.identifier.citation Hoon Yap , M , Goyal , M , Osman , F , Martí , R , Denton , E , Juette , A & Zwiggelaar , R 2018 , ' Breast ultrasound lesions recognition: : end-to-end deep learning approaches ' Journal of Medical Imaging , vol 6 , no. 1 , 011007 . DOI: 10.1117/1.JMI.6.1.011007 en
dc.identifier.issn 2329-4310
dc.identifier.other PURE: 27999110
dc.identifier.other PURE UUID: e8bb6e4e-ea3f-4502-9029-fc82052c5d35
dc.identifier.other Scopus: 85048013441
dc.identifier.other handle.net: 2160/47075
dc.identifier.uri http://hdl.handle.net/2160/47075
dc.identifier.uri https://caps.luminad.com:8443/JMI-18119SSR_online.pdf en
dc.description.abstract Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top “mean Dice” score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score >0.5, 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof Journal of Medical Imaging en
dc.rights en
dc.subject image segmentation en
dc.subject breast en
dc.subject ultrasonography en
dc.subject data modelling en
dc.subject image classification en
dc.subject tumor growth modelling en
dc.subject RGB color model en
dc.title Breast ultrasound lesions recognition: : end-to-end deep learning approaches en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version authorsversion en
dc.identifier.doi https://doi.org/10.1117/1.JMI.6.1.011007
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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