Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks
Department of Informatics, King’s College London, London WC2R 2LS, UK
College of Management, Shenzhen University, Shenzhen 518060, China
Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Authors to whom correspondence should be addressed.
Academic Editor: Maggie Chon U Cheang
Received: 20 November 2020
Revised: 30 January 2021
Accepted: 3 February 2021
Published: 7 February 2021
The assistance of computer image analysis that automatically identifies tissue or cell types has greatly improved histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neural Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. We observe that image resolutions of lymph node metastasis datasets in breast cancer usually are quite smaller than the designed model input resolution, which defects the performance of the proposed model. To mitigate this problem, we propose a boosted CNN architecture and a novel data augmentation method called Random Center Cropping (RCC). Different from traditional image cropping methods only suitable for resolution images in large scale, RCC not only enlarges the scale of datasets but also preserves the resolution and the center area of images. In addition, the downsampling scale of the network is diminished to be more suitable for small resolution images. Furthermore, we introduce attention and feature fusion mechanisms to enhance the semantic information of image features extracted by CNN. Experiments illustrate that our methods significantly boost performance of fundamental CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% in Rectified Patch Camelyon (RPCam) datasets, respectively.