Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

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dc.contributor.authorVimala, Baiju Babucs
dc.contributor.authorSrinivasan, Saravanancs
dc.contributor.authorMathivanan, Sandeep Kumarcs
dc.contributor.authorMuthukumaran, Venkatesancs
dc.contributor.authorBabu, Jyothi Chinnacs
dc.contributor.authorHerencsár, Norbertcs
dc.contributor.authorVilcekova, Luciacs
dc.coverage.issue3cs
dc.coverage.volume23cs
dc.date.issued2023-01-19cs
dc.description.abstractRapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.en
dc.formattextcs
dc.format.extent1-16cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2023, vol. 23, issue 3, p. 1-16.en
dc.identifier.doi10.3390/s23031167cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other181516cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttp://hdl.handle.net/11012/209274
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/23/3/1167cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectlocal speckle noise destructionen
dc.subjecthybrid deep learning techniqueen
dc.subjectlogical-pool recurrent neural networken
dc.subjectsignal-to-noise ratioen
dc.subjectspatial high-pass filteren
dc.subjectglandular ultrasound imageen
dc.titleImage Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Techniqueen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-181516en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:30en
sync.item.modts2025.01.17 15:17:11en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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