Application of Improved NASNet Algorithm in Breast Ultrasound Diagnosis
Yi Sanli1,2*, She Furong1,2, Yang Xuelian1,2, Chen Dong3, Luo Xiaomao3*
1(School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China) 2(Key Laboratory of Computer Technology Application of Yunnan Province, Kunming 650504, China) 3(Department of Ultrasound Medicine, Yunnan Cancer Hospital, Kunming 650118, China)
Abstract：Ultrasound image is of great significance in clinical diagnosis of breast diseases, however, the resolution of the breast ultrasound image is low, and the sample size is small. Although NASNet is suitable for small sample data, it requires many parameters that makes it difficult to train. This paper proposed an improved NASNet classification algorithm to distinguish the benign and malignant breast masses. Firstly, NASNet is pre-trained on Imagenet by transfer learning technology, and the learned features were directly used for benign and malignant tumor recognition on breast ultrasound images, which saved the cost of calculation and improved the accuracy of the calculation. Then, to enhance the ability of the network to extract ultrasonic image features and make the network lightweight, we deeply integrated deep separable convolution into NASNet to construct a large-scale network. Finally, to enhance feature weights that are more relevant to the disease and further enhance the extraction ability of high-order feature information, we added an SE module to screen the channel features that account for more weight in ultrasonic images. To verify the algorithm, we used the training method of 5-fold cross-validations based on the experiments of local hospital data sets and public data sets and compare the algorithm with the widely used classification algorithm. There were 1 350 ultrasound images in the local hospital datasets and 895 ultrasound images in the two public datasets. Based on the data of local hospitals, Acc, Sen, and F1 were 97.52%. The Acc, Sen, and F1 of experiments with public data set as training set and verification set and local hospital data set as test set were 96.31%, 96.31%, and 96.39% respectively. The Acc, Sen, and F1 of the mixed data experiment based on local hospital data and public data were 98.27%. The results showed that the improved algorithm had advantages over other algorithms and was more suitable for the classification of benign and malignant tumors with a small amount of breast ultrasound images.
易三莉, 佘芙蓉, 杨雪莲, 陈东, 罗晓茂. 改进的NASNet算法在乳腺超声诊断中的应用研究[J]. 中国生物医学工程学报, 2022, 41(6): 672-679.
Yi Sanli, She Furong, Yang Xuelian, Chen Dong, Luo Xiaomao. Application of Improved NASNet Algorithm in Breast Ultrasound Diagnosis. Chinese Journal of Biomedical Engineering, 2022, 41(6): 672-679.
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