Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extraction

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dc.contributor.authorHamza, Alics
dc.contributor.authorMézl, Martincs
dc.coverage.issueMaycs
dc.coverage.volume13cs
dc.date.accessioned2025-07-17T12:58:54Z
dc.date.available2025-07-17T12:58:54Z
dc.date.issued2025-05-09cs
dc.description.abstractBreast cancer remains a significant global health challenge, requiring accurate and effective diagnostic methods for timely treatment. Ultrasound imaging is a valuable diagnostic tool for breast cancer because of its affordability, accessibility, and non-ionizing radiation properties. This study proposes a classification method for breast ultrasound images that integrates segmentation and feature extraction. Initially, ultrasound images are pre-processed to enhance quality and reduce noise, followed by segmentation using the U-Net++ architecture. Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. The model's performance is assessed using accuracy, precision, recall, Mean IoU, and Dice Score metrics. The U-Net++ model achieved superior segmentation performance with a Dice Score of 0.911 and a Mean IoU of 0.838, outperforming related methods such as U-Net (0.888 Dice, 0.79 IoU) and Efficient U-Net (0.904 Dice, 0.80 IoU). In the classification task, MobileNetV2 when paired with the ANN classifier, produced the highest test accuracy at 96.58%, with a precision of 97% and recall of 96%. Our approach demonstrates superior performance compared to other models, such as RMTL-Net, which achieved 91.02% accuracy, and hybrid CAD models with 94% accuracy. This highlights the benefits of combining advanced segmentation and feature extraction techniques, with MobileNetV2 proving to be the better model, offering superior accuracy and robustness in classification tasks. This approach has the potential to support promise for supporting radiologists, enhance diagnostic accuracy, and ultimately improve outcomes for breast cancer patients. In the future, we will use comprehensive datasets to validate our methodology.en
dc.formattextcs
dc.format.extent83528-83541cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Access. 2025, vol. 13, issue May, p. 83528-83541.en
dc.identifier.doi10.1109/ACCESS.2025.3568588cs
dc.identifier.issn2169-3536cs
dc.identifier.orcid0000-0001-9162-7607cs
dc.identifier.orcid0000-0002-4147-8727cs
dc.identifier.other198028cs
dc.identifier.researcheridA-2336-2016cs
dc.identifier.scopus36477866400cs
dc.identifier.urihttps://hdl.handle.net/11012/255196
dc.language.isoencs
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCcs
dc.relation.ispartofIEEE Accesscs
dc.relation.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10994408&tag=1cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2169-3536/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectImage segmentationen
dc.subjectAccuracyen
dc.subjectUltrasonic imagingen
dc.subjectFeature extractionen
dc.subjectSolid modelingen
dc.subjectBreast canceren
dc.subjectConvolutional neural networksen
dc.subjectBreast tumorsen
dc.subjectSystem analysis and designen
dc.subjectDeep learningen
dc.subjectClassificationen
dc.subjectMobilenetV2en
dc.subjectsegmentationen
dc.subjectfeature extractionen
dc.titleDeep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extractionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-198028en
sync.item.dbtypeVAVen
sync.item.insts2025.07.17 14:58:53en
sync.item.modts2025.07.17 14:33:10en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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