Using convolutional neural networks for breast cancer diagnosing
Keywords:convolutional neural networks, deep learning, computer-aided detection, breast cancer diagnosis, classification
AbstractDuring the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN’s architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively.
Breast cancer: prevention and control. — Available at: https://www.who. int/cancer/detection/breastcancer/en/
Yassin N. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review / N. Yassin, S. Omran, E. El Houby, H. Allam // Computer Methods and Programs in Biomedicine. — 2018. — Vol. 156. — P. 25–45.
Keras: The Python Deep Learning library. — Available at: https://keras.io/
Sharma S. Computer-aided diagnosis of malignant mammograms using zernike moments and SVM / S. Sharma, P. Khanna // Journal of Digital Imaging. — 2015. — 28 (1). — P. 77–90.
Beheshti S.M.A. An efficient fractal method for detection and diagnosis of breast masses in mammograms / S.M.A. Beheshti et al. // Journal of Digital Imaging. — 2014. — 27 (5). — P. 661–669.
Azar A.T. Decision tree classifiers for automated medical diagnosis / A.T. Azar, S.M. El-Metwally // Neural Comput. Appl. — 2013. — 23 (7). — P. 2387–2403.
Jian W. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform / W. Jian, X. Sun, S. Luo // Biomed. Eng. Online. — 2012. — 11 (1). — p. 96.
Hiba A. Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis / A. Hiba, M. Hajar, M. Hassan, N. Thomas // The 6th International Symposium on Frontiers in Ambient and Mobile Systems. Procedia Computer Science 83. — 2016. — P. 1064–1069.
Sharmaa H. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology / H. Sharmaa, N. Zerbe, I. Klempert et al. // Computerized Medical Imaging and Graphics.— 2017. — 61. — P. 2–13.
Mohammad M. A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning / M. Mohammad, M. Hamid, M. Marjan et al. // Computational and Structural Biotechnology Journal. — 2017. — 15. — P. 75–85.
Shen Li. Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography / Li Shen, Laurie R. Margolies, Joseph H. Rothstein et al. Published online Mar 15. — doi: 10.1038/s41598-018-22437-z. 2018.
K-Fold Cross Validation. — Available at: https://medium.com/ datadriveninvestor/ K-fold -cross-validation-6b8518070833
Rakhlin A. Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis / A. Rakhlin, A. Shvets, V. Iglovikov. — ICIAR 2018 Grand Challenge. arXiv:1802.00752v2. 2018.
An Intuitive Explanation of Convolutional Neural Networks. — Available at: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
Shin Hoo-Chang. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning / Hoo-Chang Shin, Holger R. Roth, Mingchen Gao et al. // IEEE Transactions on medical imaging. — 2016. — Vol. 35, N 5.
Breast cancer dataset from breakhis. — Available at: https://www.kaggle. com/kritika397/breast-cancer-dataset-from-breakhis
Bioimaging Challenge 2015 Breast Histology Dataset. — Available at: https://rdm.inesctec.pt/dataset/nis-2017-003
Breast histopathology. — Available at: https://www.kaggle.com/ paultimothymooney/breast-histopathology-images#IDC_regular_ps50_idx5.zip
Sharmaa H. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology / H. Sharmaa, N. Zerbe, I. Klempert // Computerized Medical Imaging and Graphics. — 2017. — 61. — P. 2–13.
Image Preprocessing. — Available at: https://keras.io/preprocessing/image/