The study of ultrasonography based on deep learning in breast cancer

Abstract

ObjectiveThrough the dynamic observation, treatment, and nursing experience of patients with advanced breast cancer, we can accumulate rich clinical experience and do a better job in nursing. MethodsDeep learning radiology (DLR) extracted in-depth features from medical images through different frameworks and further analyzed the extracted deep features. The dynamic observation, treatment, and nursing of 50 patients with advanced breast cancer from hospital diagnosis provided patients with rapid, effective, and definite treatment and nursing measures. As a result, 49 patients (99%) were discharged from the hospital with improved and stable conditions. Conclusion Effective treatment and careful nursing for patients with advanced breast cancer as soon as possible will help to improve their quality of life, prolong their life cycle, reduce mortality, and assist clinical practice. ResultsCompared to traditional imaging omics, ultrasound-based DLR also needs some help, as it requires direct learning of features from data and a large dataset. Although there are more medical images than ever before, it is difficult to collect a large amount of data due to issues such as data privacy protection and complex artificial image annotation. In this comparative study, DLR can automatically extract deep features without relying on manual labeling by doctors, further improving its accuracy and reliability in tumor diagnosis and prognosis prediction. Ultrasound is the primary way of early diagnosis of breast cancer. ConclusionBy analyzing the research status of ultrasound-based DLR in the differential diagnosis of benign and malignant breast tumors, the prediction of molecular typing of breast cancer, the evaluation of axillary lymph node status, and the evaluation of neoadjuvant chemotherapy efficacy in recent years, Ultrasound-based DLR directly extracts high-order features from datasets; although it is still in the development stage, it also demonstrates its superior performance, helping to reduce workload and improve diagnostic efficiency.