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dc.contributor.authorBalasubramaniam, Sathiyabhama
dc.contributor.authorVelmurugan, Yuvarajan
dc.contributor.authorJaganathan, Dhayanithi
dc.contributor.authorDhanasekaran, Seshathiri
dc.date.accessioned2023-11-14T10:01:36Z
dc.date.available2023-11-14T10:01:36Z
dc.date.issued2023-08-24
dc.description.abstractConvolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.en_US
dc.identifier.citationBalasubramaniam, Velmurugan, Jaganathan, Dhanasekaran. A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images. Diagnostics (Basel). 2023;13(17)en_US
dc.identifier.cristinIDFRIDAID 2189197
dc.identifier.doi10.3390/diagnostics13172746
dc.identifier.issn2075-4418
dc.identifier.urihttps://hdl.handle.net/10037/31759
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalDiagnostics (Basel)
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleA Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Imagesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)