Open Access

ARTICLE

Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

Jing Lu¹, Yan Wu^2,#, Mingyan Hu¹, Yao Xiong¹, Yapeng Zhou¹, Ziliang Zhao¹, Liutong Shang^1,*

1 Department of Radiology, Fourth Medical Center of PLA General Hospital, Beijing, 100048, China
2 Qinghe Clinic, Beijing North Medical District of Chinese PLA General Hospital, Beijing, 100085, China

* Corresponding Author: Liutong Shang. Email:
# Co-first Author: Yan Wu

(This article belongs to the Special Issue: Application of Deep Learning in Medical Image Analysis (DL-MIA))

Computer Modeling in Engineering & Sciences 2022, 130(1), 365-377. https://doi.org/10.32604/cmes.2021.017897

Received 15 June 2021; Accepted 07 July 2021; Issue published 29 November 2021

Abstract

Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception V3, and ResNet50 has higher performance than mainstream CAD systems. Among them, the system built based on VGG16 and ResNet50 has outstanding performance. We further explore the impact of the secondary migration on the experimental results in the VGG16 system, and these results show that the migration can improve system performance of the proposed framework. Conclusion: The accuracy of CNN represented by VGG16 is as high as 91.25%, which is more accurate than traditional machine learning models. The F1 score of the three basic networks that join the secondary migration is close to 1.0, and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3, and ResNet50.

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Keywords

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