Abstract
This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.
Highlights
Breast cancer refers to the appearance of malignant tumors in the breast; that is, the cancer cells originate from the lobules of the ductal epithelium of the breast, and the breast cells grow out of control and are formed
Research Objects and Grouping. 90 breast cancer patients with axillary lymph node metastasis were selected in this study, who were treated in hospital from January 2017 to September 2020. e cases included in this study were subjected to pathological examination and ultrasound diagnostic examination before surgical treatment
The ultrasound images of the experimental group were processed by artificial intelligent-based ultrasound image segmentation technology to diagnose breast cancer axillary lymph nodes, while the control group was directly diagnosed by routine ultrasound images. e study had been approved by the medical ethics committee of the hospital, and the patients and their family members understood the content and methods of this study and agreed to sign the corresponding informed consent
Summary
Breast cancer refers to the appearance of malignant tumors in the breast; that is, the cancer cells originate from the lobules of the ductal epithelium of the breast, and the breast cells grow out of control and are formed. Breast cancer can be divided into carcinoma in situ and invasive carcinoma based on histopathology. According to immunohistochemistry, it can be divided into luminal A, luminal B, and triple-negative breast cancer [1]. E incidence of breast cancer is very high, ranking at the top of malignant tumors. It can be divided into luminal A, luminal B, and triple-negative breast cancer [1]. In the growth process of breast cancer, regional lymph node metastasis is prone to occur as the tumor infiltrates into the surrounding mammary gland tissues. Current ultrasound is not diagnostic for some small or unformed nodules
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