Computer-aided Tumor Diagnosis for 3-D Breast Elastography using Convolutional Neural Network

• Main Authors: Yuan-Feng Zhu, 朱元豐
• Other Authors: Ruey-Feng Chang
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/22y3ys

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === Breast cancer is the common and second leading cause of cancer death in women. Therefore, early examination and treatment are the most effective way to reduce the mortality rate. Recently, the breast elastography techniques had become a helpful examination for cancer diagnosis. As a result, the computer-aided diagnosis (CAD) system based on 3-D convolutional neural networks (CNN) using breast elastography is proposed to distinguish the tumors as benign or malignant. At first, the U-Net segmentation method is used to extract the tumor region from 3-D B-mode images. After generating the tumor region, the mask images contain complete shape features. Thus, the tumors are diagnosed in the proposed CAD systems using B-mode images, elastographic images, mask images, masked elastographic images, and masked B-mode images. In addition, the ensemble method is adopted to reduce prediction variance and enhance diagnosis performance. In this experiment, totally 83 biopsy-proved lesions composed of 63 benign tumors and 20 malignant tumors are used to evaluate our proposed method. According to results, the best diagnosis performance is using the ensemble method combined with B-mode images, mask images, and masked elastographic images and the accuracy, sensitivity, specificity, and AUC are 90.36% (75/83), 90.00% (18/20), 90.48% (57/63), and 0.9374, respectively.