Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning

• Published in: Bioengineering (Basel) 2023-10, Vol.10 (10), p.1220
• Main Authors: Yang, Lei, Zhang, Baichuan, Ren, Fei, Gu, Jianwen, Gao, Jiao, Wu, Jihua, Li, Dan, Jia, Huaping, Li, Guangling, Zong, Jing, Zhang, Jing, Yang, Xiaoman, Zhang, Xueyuan, Du, Baolin, Wang, Xiaowen, Li, Na
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; auxiliary diagnosis; Bioengineering; Breast cancer; Breast tumors; Cancer; Cancer screening; China; Classification; convolutional neural network; Datasets; Deep learning; Diagnosis; Diagnostic systems; Evaluation; Hospitals; Image analysis; Image processing; Image segmentation; Localization; Machine learning; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical screening; Methods; Neural networks; Nodules; Patients; Physicians; Recognition; Semantic segmentation; Technology application; tumor identification; Tumors; Ultrasonic imaging; Ultrasonic testing; Ultrasound; Ultrasound imaging
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10101220

Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient’s health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.