DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images

• Autorzy: Demir Fatih

Identyfikatory

DOI

10.1016/j.bbe.2021.07.004

• Języki publikacji: EN

Abstrakty

EN

The analysis of histopathological images is the core way for detecting breast cancer, the most insidious type of cancer for women. Artificial intelligence-based applications are used as an effective and supportive tool for automated breast cancer detection. Especially, deep learning models are among the most popular approaches due to their high performances in classification problems of medical images. In this study, a novel and robust approach, based on the convolutional-LSTM (CLSTM) learning model, the pre-processing technique using marker-controlled watershed segmentation algorithm (MWSA), and the optimized SVM classifier, was proposed for detecting breast cancer automatically from histopathological images (HPIs). The CLSTM model trained on the BreakHis dataset, which is popular in the research community, composes of binary and eight-class classification tasks. The classification performance of the CLSTM model was significantly increased by using the processed HPIs with MWSA. For binary and eight-class classification tasks, the best scores were obtained by using the optimized SVM classifier with Bayesian optimization instead of the softmax classifier of the CLSTM model. The proposed approach, which provided very high performance for both classification tasks, was compared to the existing approaches using the BreakHis dataset.

Słowa kluczowe

EN

breast cancer detection; HPIs; MWSA; CLSTM model; hyperparameter optimization of SVM

PL

wykrywanie raka piersi; HPIs; MWSA; model CLSTM

• 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. 3
• Strony: 1123--1139
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Demir Fatih

• Firat University, Vocational School of Technical Sciences, Biomedical Dept, Elazig, Turkey

Bibliografia

• [1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7–34.
• [2] Veta M, Pluim JPW, van Diest PJ, Viergever MA. Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 2014;61:1400–11.
• [3] Holzinger A, Malle B, Kieseberg P, Roth PM, Müller H, Reihs R, et al. Towards the augmented pathologist: challenges of explainable-ai in digital pathology. ArXiv Prepr 2017. ArXiv171206657.
• [4] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012:1097–105.
• [5] Challenge LSVR. ImageNet http://www. image-net. org/ challenges. LSVRC/2012/Results Html 2012.
• [6] ElOuassif B, Idri A, Hosni M, Abran A. Classification techniques in breast cancer diagnosis: a systematic literature review. Comput Methods Biomech Biomed Eng Imaging Vis 2021;9:50–77.
• [7] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88.
• [8] Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221–48.
• [9] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A dataset for breast cancer histopathological image classification. Ieee Trans Biomed Eng 2016;63:1455–62.
• [10] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. Breast cancer histopathological image classification using convolutional neural networks. In: 2016 Int. Jt. Conf. Neural Networks. p. 2560–7.
• [11] Bayramoglu N, Kannala J, Heikkilä J. Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd Int. Conf. Pattern Recognit.. p. 2440–5.
• [12] Wei B, Han Z, He X, Yin Y. Deep learning model based breast cancer histopathological image classification. In: 2017 IEEE 2nd Int. Conf. Cloud Comput. Big Data Anal.. p. 348–53.
• [13] Budak Ü, Cömert Z, Rashid ZN, Sengür A, Çıbuk M. Computeraided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput J 2019;85:105765. https://doi.org/ 10.1016/j.asoc.2019.105765.
• [14] Li Q, Li W. Using deep learning for breast cancer diagnosis. Hong Kong, Tech Rep: Chinese Univ Hong Kong; 2017.
• [15] Thuy MBH, Hoang VT. Fusing of deep learning, transfer learning and gan for breast cancer histopathological image classification. Int Conf Comput Sci Appl Math Appl 2019:255–66.
• [16] Bardou D, Zhang K, Ahmad SM. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 2018;6:24680–93.
• [17] Boumaraf S, Liu X, Wan Y, Zheng Z, Ferkous C, Ma X, et al. Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: a comparative study with visual explanation. Diagnostics 2021;11:528.
• [18] Song Y, Zou JJ, Chang H, Cai W. Adapting fisher vectors for histopathology image classification. 2017 IEEE 14th Int. Symp. Biomed. Imaging (ISBI 2017), 2017, p. 600–3.
• [19] Zhi W, Yueng HWF, Chen Z, Zandavi SM, Lu Z, Chung YY. Using transfer learning with convolutional neural networks to diagnose breast cancer from histopathological images. Int Conf Neural Inf Process 2017:669–76.
• [20] de Matos J, Britto AdeS, Oliveira LES, Koerich AL. Double transfer learning for breast cancer histopathologic image classification. In: 2019 Int. Jt. Conf Neural Networks. p. 1–8.
• [21] Mehra R et al. Breast cancer histology images classification: Training from scratch or transfer learning? ICT Express 2018;4:247–54.
• [22] Saxena S, Shukla S, Gyanchandani M. Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology. Int J Imaging Syst Technol 2020;30:577–91.
• [23] Gour M, Jain S, Sunil Kumar T. Residual learning based CNN for breast cancer histopathological image classification. Int J Imaging Syst Technol 2020;30:621–35.
• [24] Deniz E, Sengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Heal Inf Sci Syst 2018;6:18. https://doi.org/10.1007/s13755-018-0057-x.
• [25] Nahid A-A, Kong Y. Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 2018;9:19. https:// doi.org/10.3390/info9010019.
• [26] Toğaçar M, Özkurt KB, Ergen B, Cömert Z. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Its Appl 2020;545:123592.
• [27] Boumaraf S, Liu X, Zheng Z, Ma X, Ferkous C. A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomed Signal Process Control 2021;63:102192.
• [28] Wang P, Wang J, Li Y, Li P, Li L, Jiang M. Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing. Biomed Signal Process Control 2021;65:102341.
• [29] Wang L, Gong P, Biging GS. Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogramm Eng Remote Sensing 2004;70:351–7. https://doi.org/10.14358/PERS.70.3.351.
• [30] Huang H, Li X, Chen C. Individual tree crown detection and delineation from very-high-resolution UAV images based on bias field and marker-controlled watershed segmentation algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 2018;11:2253–62. https://doi.org/10.1109/ JSTARS.2018.2830410.
• [31] Parvati K, Prakasa Rao BS, Mariya Das M. Image segmentation using gray-scale morphology and marker-controlled watershed transformation. Discret Dyn Nat Soc 2008;2008:1–8.
• [32] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv Prepr ArXiv14091556 2014.
• [33] Demir F, Abdullah DA, Sengur A. A new deep CNN model for environmental sound classification. IEEE Access 2020;8:66529–37. https://doi.org/10.1109/ ACCESS.2020.2984903.
• [34] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd Int Conf Mach Learn ICML 2015 2015;1:448–56.
• [35] Demir F, Ismael AM, Sengur A. Classification of lung sounds with CNN model using parallel pooling structure. IEEE Access 2020;8:105376–83. https://doi.org/10.1109/ ACCESS.2020.3000111.
• [36] Demir F, Turkoglu M, Aslan M, Sengur A. A new pyramidal concatenated CNN approach for environmental sound classification. Appl Acoust 2020;170:107520. https://doi.org/ 10.1016/j.apacoust.2020.107520.
• [37] Sengür D, Siuly S. Efficient approach for EEG-based emotion recognition. Electron Lett 2020;56:1361–4. https://doi.org/ 10.1049/el.2020.2685.
• [38] Selimefendigil F, Akbulut Y, Sengur A, Oztop HF. MHD conjugate natural convection in a porous cavity involving a curved conductive partition and estimations by using Long Short-Term Memory Networks. J Therm Anal Calorim 2020;140:1457–68.
• [39] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735–80.
• [40] Nour M, Cömert Z, Polat K. A novel medical diagnosis model for COVID-19 infection detection based on deep features and bayesian optimization. Appl Soft Comput J 2020;97:106580. https://doi.org/10.1016/j.asoc.2020.106580.
• [41] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929–58.
• [42] Osowski S, Siwek K, Markiewicz T. MLP and SVM networks - A comparative study. Rep. - Helsinki Univ. Technol. Signal Process. Lab., vol. 46, 2004, p. 37–40.
• [43] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–97.
• [44] Rastogi R, Sharma S, Chandra S. Robust parametric twin support vector machine for pattern classification. Neural Process Lett 2018;47:293–323. https://doi.org/10.1007/s11063- 017-9633-3.
• [45] Bogawar PS, Bhoyar KK. An improved multiclass support vector machine classifier using reduced hyper-plane with skewed binary tree. Appl Intell 2018;48:4382–91. https://doi. org/10.1007/s10489-018-1218-y.
• [46] Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 2012;4:2951–9.
• [47] Klein A, Falkner S, Bartels S, Hennig P, Hutter F. Fast Bayesian optimization of machine learning hyperparameters on large datasets. In: Proc. 20th Int. Conf. Artif. Intell. Stat. AISTATS 2017. p. 528–36.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).