Automated diagnosis of breast ultrasonography images using deep neural networks

Qi, Xiaofeng; Zhang, Lei; Yao, Chen; Pi, Yong; Chen, Yi; Lv, Qing

doi:10.1016/j.media.2018.12.006

Cited by 197 publications

(82 citation statements)

References 16 publications

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“…A breast cancer survivability prediction model that a hybrid of Incremental Learning radial basis function Neural Network, Gaussian Process classifier and AdaBoost can achieve higher prediction accuracy than conventional classifiers. Qi et al [24] proposed a network to diagnose breast ultrasound images using deep convolutional neural networks with multi-scale kernels and skip connections for improve sensitivity and robustness of classification. The network consists of two components to identify malignant tumors and recognize solid nodules in a cascade manner, which improve classification accuracy and sensitivity.…”

Section: Related Work a Breast Cancer Classificationmentioning

confidence: 99%

A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer

Yang¹,

Gong²,

Guo³

et al. 2020

IEEE Access

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In clinical medicine, the contrast-enhanced ultrasound(CEUS) has been a commonly used imaging modality for diagnosis of breast tumor. However, most researchers in computer vision field only focus on B-mode ultrasound image which does not get good results. To improve the accuracy of classification, first, we propose a novel method, i.e., a Temporal Sequence Dual-Branch Network(TSDBN) which, for the first time, can use B-mode ultrasound data and CEUS data simultaneously. Second, we designed a new Gram matrix to model the temporal sequence, and then proposed a Temporal Sequence Regression Mechanism (TSRM), which is a novel method to extract the enhancement features from CEUS video based on the matrix. For B-mode ultrasound branch, we use the traditional ResNeXt network for feature extraction. While CEUS branch uses ResNeXt + R(2 + 1)D network as the backbone network. We propose a TSRM to learning temporal sequence relationship among frames, and design a Shuffle Temporal Sequence Mechanism(STSM) to shuffle temporal sequences, the purpose of which is to further enhance temporal information among frames. Experimental results show that the proposed TSRM could use temporal information effectively and the accuracy of TSDBN is higher than that of state-of-art approaches in breast cancer classification by nearly 4%. INDEX TERMS Breast cancer classification, temporal sequence, contrast-enhanced ultrasound (CEUS), shuffle mechanism. WENBIN LIU received the B.S. degree in communication engineering from Southwest Jiaotong University, in 2005, and the master's degree in communication and information system from the

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Section: Related Work a Breast Cancer Classificationmentioning

confidence: 99%

A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer

Yang¹,

Gong²,

Guo³

et al. 2020

IEEE Access

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“…Since 2012, when AlexNet 12 won the 2012 ILSVRC competition, 13 numerous important breakthroughs in computer vision have been achieved using DCNNs. [14][15][16] To automatically predict treatment planning for patients who need radiotherapy, and benefit from the development of DCNNs, previous studies used DCNNs to build a model to predict dose distribution. [2][3][4] The similarity of these studies is that they constructed dose prediction models based on the treatment planning of patients treated with IMRT.…”

Section: Dose Predictionmentioning

confidence: 99%

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

Wang

Zhang

et al. 2020

Int J Intell Syst

Self Cite

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Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results

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“…The use of computer‐aided detection helps radiologists analyze and highlight the suspicious cancerous regions that improve the cancer detection rate . This methodology improves the cancer detection rate by considering a two‐step guaranteed system to help the radiologist decrease human error …”

Section: Introductionmentioning

confidence: 99%

Deep learning and optimization algorithms for automatic breast cancer detection

Sha

Hu²,

Rouyendegh

2020

Int J Imaging Syst Tech

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Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-ofthe-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. K E Y W O R D S breast cancer, convolutional neural networks, feature extraction, feature selection, grasshopper optimization algorithm, image classification, image segmentation

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Automated diagnosis of breast ultrasonography images using deep neural networks

Cited by 197 publications

References 16 publications

A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer

A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

Deep learning and optimization algorithms for automatic breast cancer detection

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