Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet

It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fi...

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Detalles Bibliográficos
Autores principales: Wang, Long, Zhang, Ming, He, Guangyuan, Shen, Dong, Meng, Mingzhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047403/
https://www.ncbi.nlm.nih.gov/pubmed/36980377
http://dx.doi.org/10.3390/diagnostics13061067
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author Wang, Long
Zhang, Ming
He, Guangyuan
Shen, Dong
Meng, Mingzhu
author_facet Wang, Long
Zhang, Ming
He, Guangyuan
Shen, Dong
Meng, Mingzhu
author_sort Wang, Long
collection PubMed
description It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI).
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spelling pubmed-100474032023-03-29 Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet Wang, Long Zhang, Ming He, Guangyuan Shen, Dong Meng, Mingzhu Diagnostics (Basel) Article It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI). MDPI 2023-03-11 /pmc/articles/PMC10047403/ /pubmed/36980377 http://dx.doi.org/10.3390/diagnostics13061067 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
Wang, Long
Zhang, Ming
He, Guangyuan
Shen, Dong
Meng, Mingzhu
Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title_full Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title_fullStr Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title_full_unstemmed Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title_short Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
title_sort classification of breast lesions on dce-mri data using a fine-tuned mobilenet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047403/
https://www.ncbi.nlm.nih.gov/pubmed/36980377
http://dx.doi.org/10.3390/diagnostics13061067
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