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A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Convolutional 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 a...

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Autores principales: Balasubramaniam, Sathiyabhama, Velmurugan, Yuvarajan, Jaganathan, Dhayanithi, Dhanasekaran, Seshathiri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486538/
https://www.ncbi.nlm.nih.gov/pubmed/37685284
http://dx.doi.org/10.3390/diagnostics13172746
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author Balasubramaniam, Sathiyabhama
Velmurugan, Yuvarajan
Jaganathan, Dhayanithi
Dhanasekaran, Seshathiri
author_facet Balasubramaniam, Sathiyabhama
Velmurugan, Yuvarajan
Jaganathan, Dhayanithi
Dhanasekaran, Seshathiri
author_sort Balasubramaniam, Sathiyabhama
collection PubMed
description Convolutional 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.
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spelling pubmed-104865382023-09-09 A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images Balasubramaniam, Sathiyabhama Velmurugan, Yuvarajan Jaganathan, Dhayanithi Dhanasekaran, Seshathiri Diagnostics (Basel) Article Convolutional 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. MDPI 2023-08-24 /pmc/articles/PMC10486538/ /pubmed/37685284 http://dx.doi.org/10.3390/diagnostics13172746 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Balasubramaniam, Sathiyabhama
Velmurugan, Yuvarajan
Jaganathan, Dhayanithi
Dhanasekaran, Seshathiri
A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title_full A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title_fullStr A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title_full_unstemmed A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title_short A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
title_sort modified lenet cnn for breast cancer diagnosis in ultrasound images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486538/
https://www.ncbi.nlm.nih.gov/pubmed/37685284
http://dx.doi.org/10.3390/diagnostics13172746
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