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摘要:
Classification of breast density is significantly important during the process of breast diagnosis. The purpose of this study was to develop a useful computer-ized tool to help radiologists determine the patient’s breast density category on the mammogram. In this article, we presented a model for automatically classi-fying breast densities by employing a wavelet transform-based and fine-tuned convolutional neural network (CNN). We modified a pre-trained AlexNet model by removing the last two fully connected (FC) layers and appending two newly created layers to the remaining structure. Unlike the common CNN-based methods that use original or pre-processed images as inputs, we adopted the use of redundant wavelet coefficients at level 1 as inputs to the CNN model. Our study mainly focused on discriminating between scattered density and heterogeneously dense which are the two most difficult density cat-egories to differentiate for radiologists. The proposed system achieved 88.3% overall accuracy. In order to demonstrate the effectiveness and usefulness of the proposed method, the results obtained from a conventional fine-tuning CNN model was compared with that from the proposed method. The results demon-strate that the proposed technique is very promising to help radiologists and serve as a second eye for them to classify breast density categories in breast cancer screening.
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篇名 Using a Wavelet-Based and Fine-Tuned Convolutional Neural Network for Classification of Breast Density in Mammographic Images
来源期刊 医学影像期刊(英文) 学科 医学
关键词 MAMMOGRAPHY MAMMARY GLAND Density CNN WAVELET TRANSFORM
年,卷(期) 2020,(1) 所属期刊栏目
研究方向 页码范围 17-29
页数 13页 分类号 R73
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MAMMOGRAPHY
MAMMARY
GLAND
Density
CNN
WAVELET
TRANSFORM
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医学影像期刊(英文)
季刊
2164-2788
武汉市江夏区汤逊湖北路38号光谷总部空间
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25
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