Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network

Rui Du; Yanwei Chen; Tao Li; Liang Shi; Zhengdong Fei; Yuefeng Li

doi:10.1155/2022/7733583

Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network

J Oncol. 2022 Mar 19:2022:7733583. doi: 10.1155/2022/7733583. eCollection 2022.

Authors

Rui Du¹, Yanwei Chen¹, Tao Li², Liang Shi³, Zhengdong Fei⁴, Yuefeng Li⁵

Affiliations

¹ Department of Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
² Jiangsu Key Laboratory of Clinical Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, China.
³ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
⁴ Department of Ultrasound, Affiliated Shuyang Hospital of Xuzhou Medical University, Shuyang, Jiangsu, China.
⁵ Department of Medical Imaging, Affiliated Hospital of Jiangsu University, Zhenjiang, China.

Abstract

The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly picked from the patients' images as the independent test set to test its searching ability on suspicious tumor regions. Furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. Efficient-Det has demonstrated remarkable progress in general image recognition tasks with specific advantages of locating and identifying tumor areas simultaneously. Compared to the manual method (mean accuracy: 95.3% and 60 s per image) and traditional feature engineering method (mean accuracy: 90% and 15 s per image), our Efficient-Det has the capability of providing a competitive (mean accuracy: 92.6%) and fast (0.06 s per image) classification result. The deployment of Efficient-Det in our local breast cancer discrimination task exhibits specific applicability within real clinical workflows.