Breast tumor computer-aided detection system based on magnetic resonance imaging using convolutional neural network

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 learningmodels. 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. © 2022 Tech Science Press. All rights reserved.