Abstract
Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas complicate the detection and classification of tumours on whole slide images. An accurate diagnosis in a short time is a need for full treatment in breast cancer. A successful classification on breast cancer histopathological images will overcome the burden on the pathologist and reduce the subjectivity of diagnosis. In this study, we propose a deep convolutional neural network model. The model uses various algorithms (i.e., stochastic gradient descent, Nesterov accelerated gradient, adaptive gradient, RMSprop, AdaDelta and Adam) to compute the initial weight of the network and update the model parameters for faster backpropagation learning. In order to train the model with less hardware in a short time, we used the parallel computing architecture with Cuda-enabled graphics processing unit. The results indicate that the deep convolutional neural network model is an effective classification model with a high performance up to 99.05% accuracy value.
Similar content being viewed by others
References
Sudharshan P, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 117:103–111
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Wahab N, Khan A, Lee YS (2017) Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Biol Med 85:86–97
Wahab N, Khan A, Lee YS (2019) Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images. Microscopy 68(3):216–233
Qin C, Yao D, Shi Y, Song Z (2018) Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed Eng Online 17(1):113. https://doi.org/10.1186/s12938-018-0544-y
Khan A, Sohail A, Zahoora U, Qureshi AS (2019) A survey of the recent architectures of deep convolutional neural networks. arXiv preprint arXiv:190106032
Tan Y, Sim K, Ting F (2017) Breast cancer detection using convolutional neural networks for mammogram imaging system. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS). IEEE, pp 1–5. https://doi.org/10.1109/ICORAS.2017.8308076
Upasani N, Om H (2019) A modified neuro-fuzzy classifier and its parallel implementation on modern GPUs for real time intrusion detection. Appl Soft Comput 82:105595
Zhou SK, Greenspan H, Shen D (2017) Deep learning for medical image analysis. Academic Press, New York
Kleesiek J, Biller A, Urban G, Kothe U, Bendszus M, Hamprecht F (2014) Ilastik for multi-modal brain tumor segmentation. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp 12–17
Huh S, Ker DF, Bise R, Chen M, Kanade T (2011) Automated mitosis detection of stem cell populations in phase-contrast microscopy images. IEEE Trans Med Imaging 30(3):586–596. https://doi.org/10.1109/TMI.2010.2089384
Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35(5):1313–1321. https://doi.org/10.1109/TMI.2016.2528120
Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JA, Hermsen M, Manson QF, Balkenhol M, Geessink O (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22):2199–2210. https://doi.org/10.1001/jama.2017.14585
Filipczuk P, Fevens T, Krzyzak A, Monczak R (2013) Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans Med Imaging 32(12):2169–2178. https://doi.org/10.1109/TMI.2013.2275151
Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130. https://doi.org/10.1109/TMI.2015.2458702
Saha M, Chakraborty C, Racoceanu D (2018) Efficient deep learning model for mitosis detection using breast histopathology images. Comput Med Imaging Graph 64:29–40. https://doi.org/10.1016/j.compmedimag.2017.12.001
George YM, Zayed HH, Roushdy MI, Elbagoury BM (2014) Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst J 8(3):949–964. https://doi.org/10.1109/Jsyst.2013.2279415
Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462. https://doi.org/10.1109/TBME.2015.2496264
Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):4172. https://doi.org/10.1038/s41598-017-04075-z
Kausar T, Wang M, Idrees M, Lu Y (2019) HWDCNN: multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network. Biocybern Biomed Eng 39(4):967–982
Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Appl 545:123592
Yang Z, Ran L, Zhang S, Xia Y, Zhang Y (2019) EMS-Net: ensemble of multiscale convolutional neural networks for classification of breast cancer histology images. Neurocomputing 366:46–53
Budak Ü, Cömert Z, Rashid ZN, Şengür A, Çıbuk M (2019) Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput 85:105765
Dabeer S, Khan MM, Islam S (2019) Cancer diagnosis in histopathological image: CNN based approach. Inform Med Unlocked 16:100231
Vo DM, Nguyen N-Q, Lee S-W (2019) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 482:123–138
Yangqing Jia ES (2018) Berkeley artificial intelligence research. https://caffe.berkeleyvision.org/tutorial/solver.html
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791
Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp 1139–1147
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:160904747
Dozat T (2016) Incorporating Nesterov momentum into Adam. ICLR Workshop (1):2013–2016
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM, pp 675–678
Jia Y, Darrell T (2011) Heavy-tailed distances for gradient based image descriptors. In: Advances in Neural Information Processing Systems, pp 397–405
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp 1097–1105
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9
Nahid A-A, Kong Y (2017) Local and global feature utilization for breast image classification by convolutional neural network. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, pp 1–6
Malon CD, Cosatto E (2013) Classification of mitotic figures with convolutional neural networks and seeded blob features. J Pathol Inform 4:9. https://doi.org/10.4103/2153-3539.112694
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest in this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Burçak, K.C., Baykan, Ö.K. & Uğuz, H. A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model. J Supercomput 77, 973–989 (2021). https://doi.org/10.1007/s11227-020-03321-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-020-03321-y