人工智能
计算机科学
深度学习
模式识别(心理学)
乳腺超声检查
超声波
乳腺癌
情态动词
集成学习
人工神经网络
弹性成像
乳房成像
上下文图像分类
放射科
图像(数学)
癌症
乳腺摄影术
医学
材料科学
高分子化学
内科学
作者
Sampa Misra,Seungwan Jeon,Ravi Managuli,Ben Seiyon Lee,Gyuwon Kim,Chiho Yoon,Seung−Chul Lee,Richard G. Barr,Chulhong Kim
出处
期刊:IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:69 (1): 222-232
被引量:25
标识
DOI:10.1109/tuffc.2021.3119251
摘要
Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images.
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