Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish

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dc.authoridEKMEKCI, Dursun/0000-0002-9830-7793
dc.contributor.authorMohammed, Ahmed Dhahi
dc.contributor.authorEkmekci, Dursun
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the 'You Only Look Once' (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Then, features are extracted using the proposed CNN, leveraging multiscale parallel feature extraction capabilities while incorporating DenseNet and InceptionNet architectures. To combat the 'dead neuron' problem, the CNN architecture utilizes the 'Flatten Threshold Swish' (FTS) activation function. Additionally, the YOLO loss function has been enhanced to effectively handle lesion scale variation in mammograms. The proposed framework was thoroughly tested on two publicly available benchmarks: INbreast and CBIS-DDSM. It achieved an accuracy of 98.72% for breast cancer classification on the INbreast dataset and a mean Average Precision (mAP) of 91.15% for breast cancer detection on the CBIS-DDSM. The proposed CNN architecture utilized only 11.33 million parameters for training. These results highlight the proposed framework's ability to revolutionize vision-based breast cancer diagnosis.en_US
dc.identifier.doi10.3390/app14072680
dc.identifier.issn2076-3417
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85192511012en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app14072680
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7327
dc.identifier.volume14en_US
dc.identifier.wosWOS:001201106200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectbreast cancer detectionen_US
dc.subjectbreast cancer classificationen_US
dc.subjectmammogram screeningen_US
dc.subjectContrast Limited Adaptive Histogram Equalization (CLAHE)en_US
dc.subjectYou Only Look Once (YOLO)en_US
dc.subjectDenseNeten_US
dc.subjectInceptionNeten_US
dc.subjectFlatten Threshold Swish (FTS)en_US
dc.subjectINbreasten_US
dc.subjectCBIS-DDSMen_US
dc.titleBreast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swishen_US
dc.typeArticleen_US

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