Enhancing breast cancer detection with recurrent neural network

Yufeng Zheng; Clifford Yang; Hongyu Wang

doi:10.1117/12.2558817

21 April 2020 Enhancing breast cancer detection with recurrent neural network

Yufeng Zheng, Clifford Yang, Hongyu Wang

Proceedings Volume 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020; 113990C (2020) https://doi.org/10.1117/12.2558817
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Early-stage breast cancers are very challenging for computer-aided detection (CAD) because they are small and often blend in with surrounding tissues. One reason for the current CAD limitations may be the lack of temporal analysis. A radiologist usually uses the current and prior mammograms side by side to evaluate changes over time. We propose a CAD method for breast cancer screening using a recurrent neural network (RNN), a convolutional neural network (CNN) with follow-up scans. First, mammographic images are examined by three cascading object detectors to detect suspicious cancerous regions. This is similar to generating a region proposal. Then all regional images (one channel) are scaled to 224×224×3 and fed to a pre-trained CNN (ResNet-50 model) to extract features. The image features are extracted from a registered prior scan, a current scan, and their difference image, each of which has a dimension of 2048 prior to the fully-connected layer. Finally the features from the three images are combined to train a RNN classifier. The RNN functions as a temporal analysis, which can factor in multiple follow-up scans. Our digital mammographic database includes 102 cancerous masses, architecture distortion, and 27 healthy subjects, each of which includes two scans: current (cancerous or healthy), and prior scan (healthy typically one year before). Our experimental results show that the performance of the proposed CAD method is very promising.

Conference Presentation

Citation Download Citation

Yufeng Zheng, Clifford Yang, and Hongyu Wang "Enhancing breast cancer detection with recurrent neural network", Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990C (21 April 2020); https://doi.org/10.1117/12.2558817

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