Breast cancer detection from histopathology images using modified residual neural networks

• Autorzy: Gupta Varun , Vasudev Megha , Doegar Amit , Sambyal Nitigya

Identyfikatory

DOI

10.1016/j.bbe.2021.08.011

• Języki publikacji: EN

Abstrakty

EN

Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset.

Słowa kluczowe

EN

breast cancer detection; histopathology image; convolutional neural network; residual network; deep neural network; computer aided detection

PL

wykrywanie raka piersi; obraz histopatologiczny; sieć neuronowa konwolucyjna; sieć neuronowa głęboka; wykrywanie wspomagane komputerowo

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1272--1287
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Gupta Varun

• Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

autor

Vasudev Megha

• Department of Computer Science and Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India

autor

Doegar Amit

• Department of Computer Science and Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India

autor

Sambyal Nitigya

• Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, Punjab, India

Bibliografia

• [1] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clinic 2018;68(6):394–424.
• [2] Malvia S, Bagadi SA, Dubey US, Saxena S. Epidemiology of breast cancer in Indian women. Asia Pacific J Clin Oncol 2017;13(4):289–95.
• [3] Dundar MM, Badve S, Bilgin G, Raykar V, Jain R, Sertel O, et al. Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 2011;58(7):1977–84.
• [4] Aswathy MA, Jagannath M. Detection of breast cancer on digital histopathology images: present status and future possibilities. Inf Med Unlocked 2016;8:74–9.
• [5] Bardou D, Zhang K, Ahmad SM. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 2018;6(6):24680–93.
• [6] Bejnordi BE, Balkenhol M, Litjens G, Holland R, Bult P, Karssemeijer N, et al. Automated detection of DCIS in whole-slide H & E stained breast histopathology images. IEEE Trans Med Imaging 2016;35(9):2141–50.
• [7] Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G., Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T: NiftyNet: a deep-learning platform for medical imaging. Comp Methods Programs Biomed, 158: 113–122, 2018.
• [8] Spanhol F A, Oliveira L S, Petitjean C, and Heutte L: Breast cancer histopathological image classification using Convolutional Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2560–2567, 2016.
• [9] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 2016;63(7):1455–62.
• [10] Bayramoglu N, Kannala J, and Heikkila J: Deep learning for magnification independent breast cancer histopathology image classification. International Conference on Pattern Recognition (ICPR), 2440–2445, 2017.
• [11] Sun J, Binder A. Comparison of deep learning architectures for H&E histopathology images. In: IEEE Conference on Big Data and Analytics (ICBDA). p. 43–8.
• [12] Bejnordi BE, Lin J, Glass B, Mullooly M, Gierach GL, Sherman M E, and Beck A H: Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. International Symposium on Biomedical Imaging (ISBI), 929–932, 2017.
• [13] Araujo T, Guilherme A, Eduardo C, José R, Paulo A, Catarina E, et al. Classification of breast cancer histology images convolutional neural networks. PLoS ONE 2017;12(6):1–14.
• [14] Nawaz M, Sewissy AA, Soliman THA. Multi-class breast cancer classification using deep learning convolutional neural network. Int J Adv Comp Sci Appl, Sci Inf (SAI) Organiz 2018;9(6):316–22.
• [15] Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imag 2016;35 (1):119–30.
• [16] Feng Y, Zhang L, Yi Z. Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int J Comput Assist Radiol Surg 2018;13(2):179–91.
• [17] Jiao Z, Gao X, Wang Y, Li J. A deep feature-based framework for breast masses classification. Neurocomputing 2016;197:221–31.
• [18] Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed 2018;157:19–30.
• [19] Kooi T, Litjens G, Van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer-aided detection of mammographic lesions. Med Image Anal 2017;35:303–12.
• [20] Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 2016;127(127):248–57.
• [21] Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, et al. Whole mammogram image classification with convolutional neural networks. International Conference on Bioinformatics and Biomedical, 2017.
• [22] Xu Y, Jia Z, Wang LB, Ai Y, Zhang F, Lai M, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinf 2017;18(1):1–17.
• [23] Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HF. A: Two-phase multi-model automatic brain tumor diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process 2018;2018(1):97.
• [24] Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 2017;30(4):477–86.
• [25] Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 2016;35(5):1207–16.
• [26] Chen H, Ni D, Qin J, Li S, Yang X, Wang T, et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inf 2015;19(5):1627–36.
• [27] Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S. Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 2017;7(1):1–10.
• [28] Nahid AA, Kong Y. Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 2018;9(1):19.
• [29] Alom MZ, Yakopcic C, Taha TM, Asari VK. Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imag 2019;1–13.
• [30] Pratiher S, Chattoraj S. Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP). p. 1025–9.
• [31] Vo DM, Nguyen NQ, Lee SW. Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 2019;482:123–38.
• [32] Khan S, Islam N, Jan Z, Din IU, Rodrigues JJ. C: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett 2019.
• [33] Alqudah A, Alqudah AM. Sliding window based support vector machine system for classification of breast cancer using histopathological microscopic images. IETE J Res 2019;1–9.
• [34] Li Y, Wu J, Wu Q. Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 2019;7:21400–8.
• [35] Jannesari M. et al.: Breast cancer histopathological image classification: a deep learning approach, IEEE international conference on bioinformatics and biomedicine (BIBM), Madrid, Spain, 2018, pp. 2405–2412, doi: 10.1109/BIBM.2018.8621307.
• [36] Vial MR, Horwitz B, Ramos C, Czischke K, Eapen G, and Grosu HB: Massive haemoptysis TB or not TB. Thorax, 73(9): 894–894, 2018.
• [37] He K, Zhang X, Ren S, Sun J: Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770–778, 2016.
• [38] Smith LN. Cyclical learning rates for training neural networks. In: Winter conference on applications on computer vision (WACV). p. 464–72.
• [39] Loshchilov I, Hutter F. SGDR: Stochastic Gradient Descent with Warm Restarts, in: Proceedings of the International Conference on Learning Representations (ICLR); 2017.
• [40] Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE 2019;14(3). https://doi.org/10.1371/journal.pone.0214587e0214587.
• [41] Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 1998;10(7):1895–923.