Faster R-CNN Applied to Ultrasonic Images for Breast Lesion Detection and Classification (2020)

Introduction

According to the global cancer data report of the International Agency for Research on Cancer, breast cancer already becomes a common and widespread disease around the world and is also the leading cause of death from cancer in women. Compared with mammograms and magnetic resonance imaging, breast ultrasound imaging is a universal and inexpensive test method for breast lesion screening without additional risk from radiation. However, most radiologists still need support and assistance from the computer-aided diagnosis systems for the detection and classification of breast cancer in ultrasonic images by improving the differentiation between malignant and benign lesions. In this paper, we propose a lesion detection and classification algorithm for breast cancer using Faster Region Convolutional Neural Networks (Faster R-CNN) applied to ultrasonic images. The proposed algorithm is capable of locating breast lesions with a bounding box and characterizing the detected lesion as malignant or benign. An open-access series of ultrasonic data of breast lesions is used for training and testing the Faster R-CNN, and the examination result shows that the algorithm is capable of precisely locate and characterize the breast lesion with the accuracy of more than 95%.

Results

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Figure. 1. OASBUD raw data.

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Figure. 2. Bounding box generated around the candidate.

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Figure. 3. Detection results with confidence level.

publication

  1. Kaizhen Wei, Boyang Wang, and Jafar Saniie. Faster region convolutional neural networks applied to ultrasonic images for breast lesion detection and classification. In 2020 IEEE International Conference on Electro Information Technology (EIT), 171–174. IEEE, 2020. doi:10.1109/EIT48999.2020.9208264.