Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models

Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models

Abstract Background This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). Methods A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group...

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Bibliographic Details
Main Authors: Li Li, Changjie Pan, Ming Zhang, Dong Shen, Guangyuan He, Mingzhu Meng
Format: Article
Language:English
Published: BMC 2024-11-01
Series:BMC Medical Imaging
Subjects:
BI-RADS
Convolutional Neural Networks
Deep transfer learning
Breast lesions
Magnetic resonance imaging
Online Access:https://doi.org/10.1186/s12880-024-01484-1
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