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基于机器学习算法的乳腺超声图像分类研究
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Abstract:
乳腺癌已成为世界上最常见的癌症，对适龄女性进行乳腺筛查尤为重要。超声图像筛查在乳腺癌的筛查中占有主要地位。计算机辅助诊断技术可以帮助放射科医生实现快速和有效的乳腺超声图像诊断。因此，我们提出了一种全流程乳腺超声诊断系统。主要方法如下：首先基于U-Net网络对超声乳腺图像进行分割，并根据分割结果将图像划分为不同的病理区域，然后提取不同病理区域的特征并按照一定的规律得到12组特征。这12个特征组在4个不同的分类器上进行了测试，最终选出了最佳特征组和最佳分类器。结果表明，特征组NO. 12在LightGBM分类器上的性能最好。在测试集中，最终的准确度、敏感性和特异性分别为0.97、0.987和0.938，AUC达到0.962，这对于放射科医师提高诊断效率和准确性具有重要意义。
Breast cancer has become the most common cancer in the world. It is particularly important to provide breast screening for women of appropriate age, and the use of ultrasound during general screening is dominant, supplemented by mammography. Computer-aided diagnostic techniques can assist radiologists in achieving rapid and efficient diagnosis of breast ultrasound images. Therefore, we propose a full-flow breast ultrasound diagnostic system. Firstly, the ultrasound breast images are segmented based on the improved U-Net network framework to obtain breast tumor regions, and the images are divided into different pathological regions according to the segmentation results, and then the features of different pathological regions are extracted and the features are combined in different permutations to obtain 12 groups of features. These 12 feature groups were tested on four different classifiers, and the best feature group and the best classifier LightGBM (Light Gradient Boosting Machine) were finally selected. The results show that feature group NO. 12 has the best performance on the LightGBM classifier. In the testing set, the final accuracy, sensitivity and specificity were 0.97, 0.987 and 0.938, respectively. The AUC reached 0.962. This is of significance for radiologists to improve the efficiency and accuracy of diagnosis.
| [1] | Sung, H., Ferlay, J., Siegel, R.L., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249.
https://doi.org/10.3322/caac.21660 |
| [2] | Winkler, N.S., Raza, S., Mackesy, M. and Birdwell, R.L. (2015) Breast Density: Clinical Implications and Assessment Methods. Radiographics, 35, 316-324. https://doi.org/10.1148/rg.352140134 |
| [3] | Machida, Y., Tozaki, M., Shimauchi, A. and Yoshida, T. (2015) Breast Density: The Trend in Breast Cancer Screening. Breast Cancer, 22, 253-261. https://doi.org/10.1007/s12282-015-0602-2 |
| [4] | Linver, M.N. (2008) 4-19 Mammographic Density and the Risk and Detection of Breast Cancer. Breast Diseases, 18, 364-365. https://doi.org/10.1016/S1043-321X(07)80400-0 |
| [5] | Brem, R.F., Lenihan, M.J., Lieberman, J. and Torrente, J. (2015) Screening Breast Ultrasound: Past, Present, and Future. American Journal of Roentgenology, 204, 234-240. https://doi.org/10.2214/AJR.13.12072 |
| [6] | Thigpen, D., Kappler, A. and Brem, R. (2018) The Role of Ultrasound in Screening Dense Breasts—A Review of the Literature and Practical Solutions for Implementation. Diagnostics, 8, 1-14.
https://doi.org/10.3390/diagnostics8010020 |
| [7] | Chen, H.L., Zhou, J.Q., Chen, Q. and Deng, Y.C. (2021) Comparison of the Sensitivity of Mammography, Ultrasound, Magnetic Resonance Imaging and Combinations of These Imaging Modalities for the Detection of Small (≤ 2？ cm) Breast Cancer. Medicine (Baltimore), 100, e26531. https://doi.org/10.1097/MD.0000000000026531 |
| [8] | Halalli, B. and Makandar, A. (2018) Computer Aided Diagnosis—Medical Image Analysis Techniques. In: Breast Imaging, InTech, Rijeka, 1854-1858. https://doi.org/10.5772/intechopen.69792 |
| [9] | Pharswan, R. and Singh, J. (2020) Performance Analysis of SVM and KNN in Breast Cancer Classification: A Survey. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-030-39119-5_9 |
| [10] | Jain, S. and Virmani, J. (2020) Texture Ratio Vector Technique for the Classification of Breast Lesions Using SVM. Springer, Singapore. https://doi.org/10.1007/978-981-15-2740-1_14 |
| [11] | Tu, M.C., Shin, D. and Shin, D. (2009) A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms. 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, Chengdu, 12-14 December 2009, 183-187. |
| [12] | Tabrizchi, H., Tabrizchi, M. and Tabrizchi, H. (2020) Breast Cancer Diagnosis Using a Multi-Verse Optimizer-Based Gradient Boosting Decision Tree. SN Applied Sciences, 2, 1-19. https://doi.org/10.1007/s42452-020-2575-9 |
| [13] | Shelhamer, E., Long, J. and Darrell, T. (2017) Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651.
https://doi.org/10.1109/TPAMI.2016.2572683 |
| [14] | Mishra, D., Chaudhury, S., Sarkar, M. and Soin, A.S. (2019) Ultrasound Image Segmentation: A Deeply Supervised Network with Attention to Boundaries. IEEE Transactions on Biomedical Engineering, 66, 1637-1648.
https://doi.org/10.1109/TBME.2018.2877577 |
| [15] | Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, 5-9 October 2015, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28 |
| [16] | Weng, Y., Zhou, T., Li, Y. and Qiu, X. (2019) NAS-Unet: Neural Architecture Search for Medical Image Segmentation. IEEE Access, 7, 44247-44257. https://doi.org/10.1109/ACCESS.2019.2908991 |
| [17] | Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al. (2021) nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation. Nature Methods, 18, 203-211. https://doi.org/10.1038/s41592-020-01008-z |
| [18] | Ke, G., Meng, Q., Finley, T., et al. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 3147-3155. |
| [19] | Friedman, J.H. (2001) Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29, 1189-1232. https://doi.org/10.1214/aos/1013203451 |
| [20] | Chen, T. and Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 785-794.
https://doi.org/10.1145/2939672.2939785 |
| [21] | Fitzgibbon, A.W., Pilu, M. and Fisher, R.B. (1996) Direct Least Squares Fitting of Ellipses. Proceedings—International Conference on Pattern Recognition, Vol. 1, 253-257. https://doi.org/10.1109/ICPR.1996.546029 |
| [22] | Dinstein, I., Shanmugam, K. and Haralick, R.M. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610-621. https://doi.org/10.1109/TSMC.1973.4309314 |
| [23] | Al-Dhabyani, W., Gomaa, M., Khaled, H. and Fahmy, A. (2020) Dataset of Breast Ultrasound Images. Data in Brief, 28, Article ID: 104863. https://doi.org/10.1016/j.dib.2019.104863 |
| [24] | Robert, T. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x |
| [25] | Peterson, L. (2009) K-Nearest Neighbor. Scholarpedia, 4, 1883. https://doi.org/10.4249/scholarpedia.1883 |
| [26] | 王楠, 王远军, 廉朋. 基于影像组学的直肠癌术前T分期预测[J]. 波谱学杂志, 2022, 39(1): 43-55. |
| [27] | He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 770-778.
https://doi.org/10.1109/CVPR.2016.90 |
| [28] | Simonyan, K. and Zisserman, A. (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015, San Diego, 7-9 May 2015, 1-14. |
| [29] | Szegedy, C., et al. (2015) Going Deeper with Convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 7-12 June 2015, 1-9. |
| [30] | Iandola, F.N., Han, S., Moskewicz, M.W., et al. (2016) SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < 0.5 MB Model Size. http://arxiv.org/abs/1602.07360 |
| [31] | Huang, G., Liu, Z., Laurens, V., et al. (2016) Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 2261-2269.
https://doi.org/10.1109/CVPR.2017.243 |
| [32] | Tan, M. and Le, Q.V. (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, Long Beach, 10-15 June 2019, 10691-10700. |
| [33] | Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B. (2021) Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE International Conference on Computer Vision, Montreal, 11-17 October 2021, 9992-10002. https://doi.org/10.1109/ICCV48922.2021.00986 |
| [34] | Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2021) An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations, Sydney, 30-31 August 2024, 1-21. |
| [35] | Jing, X.-Y., Zhang, X., Zhu, X., et al. (2021) Multiset Feature Learning for Highly Imbalanced Data Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 139-156.
https://doi.org/10.1109/TPAMI.2019.2929166 |
