- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | GONG Xun, ZHU Dan, YANG Ziqi, LUO Jun. Low-Scale Morphological Feature Recalibration Method for Breast Ultrasound Classification[J]. Journal of Southwest Jiaotong University, 2024, 59(3): 539-546, 563. doi: 10.3969/j.issn.0258-2724.20211061
|
Breast ultrasound images have large intra-class differences, small inter-class differences, and complex and variable nodule shapes. In order to address these issues, a breast ultrasound image algorithm with morphological feature recalibration was designed to realize automatic diagnosis of breast ultrasound. First, an end-to-end network model was built, which adopted progressive training to fully learn the more discriminative regions in the image and obtain more fine-grained feature information. Secondly, a partition shuffle mechanism was proposed to reduce the noise caused by the disruption of the nodule region when the image was shuffled. Then, the features extracted from the bottom layer of the model were recalibrated with the morphological features obtained through the mask image, and a low-scale recalibration loss function was proposed. Finally, in order to verify the effectiveness of the proposed method, a low-scale recalibration database (LSRD) containing 1 550 breast ultrasound images was constructed. The experimental results show that the accuracy of the proposed model on LSRD is 94.3%; the sensitivity is 91.2%; the specificity is 93.6%, and the area (AUC) under the receiver operator characteristic curve (ROC) is 0.941, all of which are superior to other comparison models. On the breast ultrasound image (BUSI) dataset, compared with the other models, the classification accuracy of the proposed model is improved by 3.3%.
| [1] |
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: a Cancer Journal for Clinicians, 2021, 71(3): 209-249. doi: 10.3322/caac.21660
|
| [2] |
HOOLEY R J, SCOUTT L M, PHILPOTTS L E. Breast ultrasonography: state of the art[J]. Radiology, 2013, 268(3): 642-659. doi: 10.1148/radiol.13121606
|
| [3] |
龚勋,杨菲,杜章锦,等. 甲状腺、乳腺超声影像自动分析技术综述[J]. 软件学报,2020,31(7): 2245-2282.
GONG Xun, YANG Fei, DU Zhangjin, et al. Survey of automatic ultrasonographic analysis for thyroid and breast[J]. Journal of Software, 2020, 31(7): 2245-2282.
|
| [4] |
LO C M, CHANG Y C, YANG Y W, et al. Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography[J]. Computers in Biology and Medicine, 2015, 64: 91-100. doi: 10.1016/j.compbiomed.2015.06.013
|
| [5] |
FLORES W G, DE ALBUQUERQUE PEREIRA W C, INFANTOSI A F C. Improving classification performance of breast lesions on ultrasonography[J]. Pattern Recognition, 2015, 48(4): 1125-1136. doi: 10.1016/j.patcog.2014.06.006
|
| [6] |
任丽,刘洋洋,童莹,等. 乳腺肿瘤超声图像的多特征提取及分类研究[J]. 中国医疗器械杂志,2020,44(4): 294-301.
REN Li, LIU Yangyang, TONG Ying, et al. Multi-feature extraction and classification of breast tumor in ultrasound image[J]. Chinese Journal of Medical Instrumentation, 2020, 44(4): 294-301.
|
| [7] |
SPANHOL F A, OLIVEIRA L S, PETITJEAN C, et al. Breast cancer histopathological image classification using convolutional neural networks[C]//2016 International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE, 2016: 2560-2567.
|
| [8] |
WEI B Z, HAN Z Y, HE X Y, et al. Deep learning model based breast cancer histopathological image classification[C]//2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). Chengdu: IEEE, 2017: 348-353.
|
| [9] |
XIE J, SONG X S, ZHANG W, et al. A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors[J]. Physics in Medicine and Biology, 2020, 65(24): 245001.1-245001.15.
|
| [10] |
CAO Z T, DUAN L X, YANG G W, et al. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures[J]. BMC Medical Imaging, 2019, 19(1): 51.1-51.9.
|
| [11] |
孔小函,檀韬,包凌云,等. 基于卷积神经网络和多信息融合的三维乳腺超声分类方法[J]. 中国生物医学工程学报,2018,37(4): 414-422.
KONG Xiaohan, TAN Tao, BAO Lingyun, et al. Classification of breast mass in 3D ultrasound images with annotations based on convolutional neural networks[J]. Chinese Journal of Biomedical Engineering, 2018, 37(4): 414-422.
|
| [12] |
迟剑宁,于晓升,张艺菲. 融合深度网络和浅层纹理特征的甲状腺结节癌变超声图像诊断[J]. 中国图象图形学报,2018,23(10): 1582-1593.
CHI Jianning, YU Xiaosheng, ZHANG Yifei. Thyroid nodule malignantrisk detection in ultrasound image by fusing deep and texture features[J]. Journal of Image and Graphics, 2018, 23(10): 1582-1593.
|
| [13] |
杜章锦,龚勋,罗俊,等. 乳腺超声图像中易混淆困难样本的分类方法[J]. 中国图象图形学报,2020,25(7): 1490-1500.
DU Zhangjin, GONG Xun, LUO Jun, et al. Classification method for samples that are easy to be confused in breast ultrasound images[J]. Journal of Image and Graphics, 2020, 25(7): 1490-1500.
|
| [14] |
DU R Y, CHANG D L, BHUNIA A K, et al. Fine-grained visual classification via progressive multi-granularity training of jigsaw patches[C]//European Conference on Computer Vision. Cham: Springer, 2020: 153-168.
|
| [15] |
杨丽娜. 乳腺癌超声图像报告中的BI-RADS与术后病理结果之间的相关性[J]. 世界复合医学,2018,4(6): 51-53.
YANG Li’na. Correlation between BI-RADS and postoperative pathological findings in breast cancer ultrasound image reports[J]. World Journal of Complex Medicine, 2018, 4(6): 51-53.
|
| [16] |
LAMPLE G, OTT M, CONNEAU A, et al. Phrase-based & neural unsupervised machine translation[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018: 5039-5049.
|
| [17] |
CHEN Y, BAI Y L, ZHANG W, et al. Destruction and construction learning for fine-grained image recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 5152-5161.
|
| [18] |
AL-DHABYANI W, GOMAA M, KHALED H, et al. Dataset of breast ultrasound images[J]. Data in Brief, 2020, 28: 104863.1-104863.5.
|
| [19] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-14)[2021-09-02]. https://ar5iv.labs.arxiv.org/html/1409.1556.
|
| [20] |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
|
| [21] |
WEI M W, DU Y Z, WU X M, et al. A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images[J]. Computational and Mathematical Methods in Medicine, 2020, 2020: 5894010.1-5894010.12.
|
| [22] |
ABED MOHAMMED M, AL-KHATEEB B, RASHID A N, et al. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images[J]. Computers & Electrical Engineering, 2018, 70: 871-882.
|
| [23] |
SHIN S Y, LEE S, YUN I D, et al. Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images[J]. IEEE Transactions on Medical Imaging, 2019, 38(3): 762-774. doi: 10.1109/TMI.2018.2872031
|
Figures(7) / Tables(9)
DownLoad:
