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Current Medical Imaging

Author(s): Inzamam Mashood Nasir, Muhammad Rashid, Jamal Hussain Shah*, Muhammad Sharif, Muhammad Yahiya Haider Awan and Monagi H. Alkinani

DOI: 10.2174/1573405616666200423085826

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An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

Page: [136 - 147] Pages: 12

  • * (Excluding Mailing and Handling)

Abstract

Background: Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images.

Objective: Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system.

Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method.

Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded.

Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

Keywords: Breast cancer, histopathological images, transfer learning, deep convolutional neural network, medical imaging, WHO.

Graphical Abstract

[1]
DeSantis C, Ma J, Bryan L, Jemal A. Breast cancer statistics, 2013. CA Cancer J Clin 2014; 64(1): 52-62.
[http://dx.doi.org/10.3322/caac.21203] [PMID: 24114568]
[2]
Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009; 2: 147-71.
[http://dx.doi.org/10.1109/RBME.2009.2034865] [PMID: 20671804]
[3]
Sharma S, Mehra R. Automatic Magnification Independent Classification of Breast Cancer Tissue in Histological Images Using Deep Convolutional Neural Network. International Conference on Advanced Informatics for Computing Research. 2019; 772-81.
[4]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69(1): 7-34.
[http://dx.doi.org/10.3322/caac.21551] [PMID: 30620402]
[5]
Basavanhally AN, Ganesan S, Agner S, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2010; 57(3): 642-53.
[http://dx.doi.org/10.1109/TBME.2009.2035305] [PMID: 19884074]
[6]
Fernández-Carrobles MM, Bueno G, Déniz O, Salido J, García-Rojo M, González-López L. Influence of texture and colour in breast TMA classification. PLoS One 2015; 10(10): e0141556.
[http://dx.doi.org/10.1371/journal.pone.0141556] [PMID: 26513238]
[7]
Amaral T, McKenna S, Robertson K, Thompson A. Classification of breast-tissue microarray spots using colour and local invariants. 2008 ISBI 2008 5th IEEE International Symposium on Biomedical Imaging From Nano to Macro. Paris, France.
[http://dx.doi.org/10.1109/ISBI.2008.4541167]
[8]
Tabesh A, Teverovskiy M. Tumor classification in histological images of prostate using color texture. 2006 ACSSC'06 Fortieth Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA.
[http://dx.doi.org/10.1109/ACSSC.2006.354868]
[9]
Kothari S, Phan JH, Stokes TH, Osunkoya AO, Young AN, Wang MD. Removing batch effects from histopathological images for enhanced cancer diagnosis. IEEE J Biomed Health Inform 2014; 18(3): 765-72.
[http://dx.doi.org/10.1109/JBHI.2013.2276766] [PMID: 24808220]
[10]
Cho H, Lim S, Choi G, Min H. Neural stain-style transfer learning using gan for histopathological images 2017. arXiv preprint arXiv:1710.08543,
[11]
Sadigh G, Carlos RC, Neal CH, Dwamena BA. Ultrasonographic differentiation of malignant from benign breast lesions: a meta-analytic comparison of elasticity and BIRADS scoring. Breast Cancer Res Treat 2012; 133(1): 23-35.
[http://dx.doi.org/10.1007/s10549-011-1857-8] [PMID: 22057974]
[12]
EtehadTavakol M. Breast cancer detection from thermal images using bispectral invariant features. Int J Therm Sci 2013; 69: 21-36.
[http://dx.doi.org/10.1016/j.ijthermalsci.2013.03.001]
[13]
Hou X, Wang G, Su G, Wang X, Nie S. Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features. Food Chem 2019; 280: 139-45.
[http://dx.doi.org/10.1016/j.foodchem.2018.12.031] [PMID: 30642479]
[14]
Gola J, Webel J, Britz D, et al. Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Comput Mater Sci 2019; 160: 186-96.
[http://dx.doi.org/10.1016/j.commatsci.2019.01.006]
[15]
Jaafari A, Pourghasemi HR. Factors Influencing Regional-Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine.Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier 2019; pp. 607-19.
[16]
Subasi A, Ahmed A, Aličković E, Hassan AR. Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform. Biomed Signal Process Control 2019; 49: 231-9.
[http://dx.doi.org/10.1016/j.bspc.2018.12.011]
[17]
Gou J, Ma H, Ou W, Zeng S, Rao Y, Yang H. A generalized mean distance-based k-nearest neighbor classifier. Expert Syst Appl 2019; 115: 356-72.
[http://dx.doi.org/10.1016/j.eswa.2018.08.021]
[18]
Zhang Y, Cao G, Wang B, Li X. A novel ensemble method for k-nearest neighbor. Pattern Recognit 2019; 85: 13-25.
[http://dx.doi.org/10.1016/j.patcog.2018.08.003]
[19]
Njuguna SN, Ondimu S, Kenji GM. Classification of drying methods for macadamia nuts based on the glcm texture parameters. In: 2018 Annual Sustainable Research and Innovation (SRI) Conference.
[20]
Mukhopadhyay S, Pratiher S, Mukherjee S, Dasgupta D, Ghosh N, Panigrahi PK. A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis.High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management. International Society for Optics and Photonics 2018.
[21]
Rashid M, Khan MA, Sharif M, Raza M, Sarfraz MM, Afza F. Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools Appl 2019; 78: 15751-77.
[22]
Lakshmi TV, Reddy CVK. Object Classification Using SIFT Algorithm and Transformation Techniques.Cognitive Informatics and Soft Computing. Springer 2019; pp. 403-8.
[23]
Zeng J, Zhai Y, Feng W, Chen Y, Gan J, Wang F. A novel finger-vein recognition based on quality assessment and multi-scale histogram of oriented gradients feature. Int J Enterprise Inf Syst 2019; 15(1): 100-15.
[24]
Reichman D, Collins LM, Malof JM. gprHOG: Several simple improvements to the histogram of oriented gradients feature for threat detection in ground-penetrating radar 2018. arXiv preprint arXiv:1806.01349.
[25]
Tasdemir SBY, Tasdemir K, Aydin Z. ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando, FL, USA.
[26]
Bhateja V, Gautam A, Tiwari A, et al. Haralick Features-Based Classification of Mammograms Using SVM.Information Systems Design and Intelligent Applications. Springer 2018; pp. 787-95.
[http://dx.doi.org/10.1007/978-981-10-7512-4_77]
[27]
Elias SJ, Hatim SM, Hassan NA, et al. Face recognition attendance system using Local Binary Pattern (LBP). Bull Elect Eng Informatics 2019; 8(1): 239-45.
[http://dx.doi.org/10.11591/eei.v8i1.1439]
[28]
Ruichek Y. Attractive-and-repulsive center-symmetric local binary patterns for texture classification. Eng Appl Artif Intell 2019; 78: 158-72.
[http://dx.doi.org/10.1016/j.engappai.2018.11.011]
[29]
Kumar TS, Kanhangad V. Gabor filter-based one-dimensional local phase descriptors for obstructive sleep apnea detection using single-lead ECG. IEEE Sensors Letters 2018; 2(1): 1-4.
[http://dx.doi.org/10.1109/LSENS.2018.2807584]
[30]
Bekhouche SE, Ouafi A, Benlamoudi A, Taleb-Ahmed A, Hadid A. Facial age estimation and gender classification using multi level local phase quantization. 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT). Tlemcen, Algeria.
[http://dx.doi.org/10.1109/CEIT.2015.7233141]
[31]
Paramkusham S, Rao KM, Rao BP. Comparison of rotation invariant local frequency, LBP and SFTA methods for breast abnormality classification. Int J Signal Imaging Syst Eng 2018; 11(3): 136-50.
[http://dx.doi.org/10.1504/IJSISE.2018.093266]
[32]
Amin J, Sharif M, Raza M, Yasmin M. Detection of brain tumor based on features fusion and machine learning. J Ambient Intell Humaniz Comput 2018; 1-17.
[http://dx.doi.org/10.1007/s12652-018-1092-9]
[33]
Khan MA, Akram T, Sharif M, et al. CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 2018; 155: 220-36.
[http://dx.doi.org/10.1016/j.compag.2018.10.013]
[34]
Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 2018; 47: 45-67.
[http://dx.doi.org/10.1016/j.media.2018.03.006] [PMID: 29679847]
[35]
Vivanti R, Ephrat A, Joskowicz L, Karaaslan OA, Lev-Cohain N, Sosna J. Automatic liver tumor segmentation in follow-up CT studies using convolutional neural networks. In: Proc Patch-Based Methods in Medical Image Processing Workshop. 1-9.
[36]
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86(11): 2278-324.
[http://dx.doi.org/10.1109/5.726791]
[37]
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.
[http://dx.doi.org/10.1109/TBME.2015.2496264] [PMID: 26540668]
[38]
Spanhol FA, et al. Breast cancer histopathological image classification using convolutional neural networks. 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver, BC, Canada.
[http://dx.doi.org/10.1109/IJCNN.2016.7727519]
[39]
Bayramoglu N, Kannala J, Heikkilä J. Deep learning for magnification independent breast cancer histopathology image classification. 2016 23rd International Conference on Pattern Recognition (ICPR). Cancun, Mexico.
[http://dx.doi.org/10.1109/ICPR.2016.7900002]
[40]
Gupta V, Bhavsar A. An Integrated Multi-scale Model for Breast Cancer Histopathological Image Classification with Joint Colour-Texture Features. International Conference on Computer Analysis of Images and Patterns. 2017.
[http://dx.doi.org/10.1007/978-3-319-64698-5_30]
[41]
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): 4172.
[http://dx.doi.org/10.1038/s41598-017-04075-z] [PMID: 28646155]
[42]
Gupta V, Bhavsar A. Breast cancer histopathological image classification: is magnification important? IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2017; Honolulu, HI, USA.
[http://dx.doi.org/10.1109/CVPRW.2017.107]
[43]
Song Y, Zou JJ, Chang H, Cai W. Adapting fisher vectors for histopathology image classification. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia.
[http://dx.doi.org/10.1109/ISBI.2017.7950592]
[44]
Song Y, Chang H, Huang H, Cai W. . Supervised intra-embedding of fisher vectors for histopathology image classification. International Conference on Medical Image Computing and Computer-Assisted Intervention. Pp. 99-106.
[http://dx.doi.org/10.1007/978-3-319-66179-7_12]
[45]
Dalal N, Triggs B. Histograms of oriented gradients for human detection. 2005 CVPR 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA, USA.
[http://dx.doi.org/10.1109/CVPR.2005.177]
[46]
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002; 24(7): 971-87.
[http://dx.doi.org/10.1109/TPAMI.2002.1017623]
[47]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition 2014. arXiv preprint arXiv:1409.1556,
[48]
Hu F, Xia GS, Hu J, Zhang L. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 2015; 7(11): 14680-707.
[http://dx.doi.org/10.3390/rs71114680]
[49]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015; Boston, MA, USA.
[50]
Chen H, Dou Q, Wang X, Qin J, Heng PA. Mitosis detection in breast cancer histology images via deep cascaded networks. Thirtieth AAAI Conference on Artificial Intelligence. 2016; 1160-6.
[51]
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer 2016. arXiv preprint arXiv:1606.05718.
[52]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012.
[53]
Spanhol FA, Oliveira LS, Petitjean C, Heutte L. Breast cancer histopathological image classification using convolutional neural networks. 2016 International Joint Conference on Neural Networks (IJCNN).
[http://dx.doi.org/10.1109/IJCNN.2016.7727519]
[54]
Li L, Pan X, Yang H, et al. Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images. Multimedia Tools Appl 2020; 79: 14509-28.
[http://dx.doi.org/10.1007/s11042-018-6970-9]
[55]
Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst 2018; 6(1): 18.
[http://dx.doi.org/10.1007/s13755-018-0057-x] [PMID: 30279988]
[56]
Feng Y, Zhang L, Mo J. Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Trans Comput Biol Bioinformatics 2018; 17(1): 91-101.
[57]
Erfankhah H, Yazdi M, Babaie M, Tizhoosh HR. Heterogeneity-aware local binary patterns for retrieval of histopathology images. IEEE Access 2019; 7: 18354-67.
[http://dx.doi.org/10.1109/ACCESS.2019.2897281]
[58]
Lichtblau D, Stoean C. Cancer diagnosis through a tandem of classifiers for digitized histopathological slides. PLoS One 2019; 14(1): e0209274.
[http://dx.doi.org/10.1371/journal.pone.0209274] [PMID: 30650087]
[59]
Sudharshan P, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P. Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 2019; 117: 103-11.
[http://dx.doi.org/10.1016/j.eswa.2018.09.049]