Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network. Issue 2 (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Yu-Dong; Satapathy, Suresh Chandra; Guttery, David S.; Górriz, Juan Manuel; Wang, Shui-Hua
• Is Part Of: Information processing & management. Volume 58:Issue 2(2021)
• Publication Date: 2021-03
• Subjects: Convolutional neural network -- Graph convolutional network -- Breast cancer classification -- Mammogram -- Artificial intelligence -- Deep learning -- Rank-based stochastic pooling -- Data augmentation; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102439
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Aim: In a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN). Method: We utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method. Results: As proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%. Conclusion: Our BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.