Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

• Published in: Applied sciences 2023-05, Vol.13 (11), p.6577
• Main Authors: Qin, Haojun, Zhang, Lei, Guo, Quan
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Breast cancer; Classification; computer-aided diagnosis; Datasets; Deep learning; Diagnosis; Diagnostic systems; Feature selection; Image classification; image generation; Image quality; Malignancy; Mammography; Medical imaging; Methods; Model accuracy; Neural networks; Noise; Patients; Support vector machines; Tumors; Ultrasonic imaging; Ultrasonic testing; Ultrasound; ultrasound reports; X-rays
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13116577

Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports.