Md. Rakibul Islam,Shariful Islam,Md. Shahadot Hosen (Rony) ,Md. Nur Alam,



Machine learning,Support Vector Machine (SVM),K-Nearest Neighbor (KNN),RESNET (Residual Network) model,Random Forest.[VII],


                  Breast cancer is a serious trouble and one of the greatest causes of death for women throughout the world. Computer-aided diagnosis (CAD) techniques can help the doctor make more credible decisions. We have determined the possibility of knowledge transfer from natural to histopathological [IX][XII] images by employing a pre-trained network ResNet-50.This pre-trained network has been utilized as a feature generator and extracted features are used to train support vector machine (SVM), random forest, decision tree, and K nearest neighbor(KNN) classifiers[X]. We altered the softmax layer to support the vector machine classifier, random forest classifier, decision tree classifier, and k-nearest neighbor classifier, to evaluate the classifier performance of each algorithm. These approaches are applied for breast cancer classification and evaluate the performance and behavior of different classifiers on a publicly available dataset named Bttheeak-HIS dataset. In order to increase the efficiency of the ResNet[III] model, we preprocessed the data before feeding it to the network. Here we have applied to sharpen filter and data augmentation techniques, which are very popular and effective image pre-processing techniques used in deep models.


I. Altman, N. S. (1992). “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression” (Pdf). The American Statistician.
II. Araújo, Teresa, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, and Aurélio Campilho. “Classification of breast cancer histology images using Convolutional Neural Networks.” PloS one 12, no. 6 (2017): e0177544.
III. “Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception,” Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069612 (13 April 2018);
IV. Balestriero, R. Neural Decision Trees. Arxiv E-Prints, 2017.
V. Bayramoglu, Neslihan, Juho Kannala, and Janne Heikkilä. “Deep learning for magnification independent breast cancer histopathology image classification.” In Pattern Recognition (ICPR), 2016 23rd International Conference on, pp. 2440- 2445. IEEE, 2016.
VI. B. E. Bejnordi, G. Zuidhof, M. Balkenhol et al., “Contextaware stacked convolutional neural networks for classifcation of breast carcinomas in whole-slide histopathology images,” Journal of Medical Imaging, vol. 4, no. 04, p. 1, 2017.
VII. Diaz-Uriarte R, Alvarez De Andres S: Gene Selection And Classification Of Microarray Data Using Random Forest. Bmc Bioinformatics 2006, 7:3.

VIII. George, Yasmeen Mourice, Hala Helmy Zayed, Mohamed Ismail Roushdy, and Bassant Mohamed Elbagoury. “Remote computer-aided breast cancer detection and diagnosis system based on cytological images.” IEEE Systems Journal 8, no. 3 (2014): 949-964.

IX. Gupta, Vibha, and Arnav Bhavsar. “Breast Cancer Histopathological Image Classification: Is Magnification Important?.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 17- 24. 2017.
X. Hall P, Park Bu, Samworth Rj (2008). “Choice Of Neighbor Order In Nearest-Neighbor Classification”. Annals Of Statistics. 36 (5): 2135–2152.
XI. Hammer B, Gersmann K: A Note On The Universal Approximation Capability Of Support Vector Machines. Neural Processing Letters 2003, 17:43-53.
XII. Han, Zhongyi, Benzheng Wei, Yuanjie Zheng, Yilong Yin, Kejian Li, and Shuo Li. “Breast cancer multi-classification from histopathological images with structured deep learning model.” Scientific reports 7, no. 1 (2017): 4172
XIII. Kahya, Mohammed Abdulrazaq, Waleed Al-Hayani, and Zakariya Yahya Algamal. “Classification of breast cancer histopathology images based on adaptive sparse support vector machine.” Journal of Applied Mathematics and Bioinformatics 7.1 (2017): 49
XIV. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun,”Deep Residual Learning for Image Recognition,” arXiv:1512.03385 [cs.CV], 10 Dec 2015;
XV. Kowal, Marek, Paweł Filipczuk, Andrzej Obuchowicz, Józef Korbicz, and Roman Monczak. “Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.” computers in biology and medicine 43, no. 10 (2013): 1563-1572.
XVI. Mehdi Habibzadeh, Mahboobeh Jannesari, Zahra Rezaei, Hossein Baharvand, Mehdi Totonchi,
XVII. Pan, S.J. And Yang, Q., 2010. A Survey On Transfer Learning. Ieee Transactions On Knowledge And Data Engineering, 22(10), Pp.1345–1359.
XVIII. Prinzie, A., Van Den Poel, D. (2008). “Random Forests For Multiclass Classification: Random Multinomial Logit”. Expert Systems With Applications
XIX. Qicheng Lao, Thomas Fevens,”Cell Phenotype Classification using Deep Residual Network and its Variants,” International Journal of Pattern Recognition and Artificial Intelligence © World Scientific Publishing Company, 01/17/19;
XX. Rawat, W. And Wang, Z., 2017. Deep Convolutional Neural Networks For Image Classification: A Comprehensive Review. Neural Computation, 29(9), Pp.2352–2449.

XXI. Rokach, Lior; Maimon, O. (2008). Data Mining With Decision Trees: Theory And Applications. World Scientific Pub Co Inc.
XXII. Shalev-Shwartz, Shai; Ben-David, Shai (2014). “18. Decision Trees”. Understanding Machine Learning. Cambridge University Press.
XXIII. Scornet, Erwan (2015). “Random Forests And Kernel Methods”.

XXIV. Simonyan, K. And Zisserman, A., 2014. Very Deep Convolutional Networks For Large-Scale Image Recognition. Arxiv Preprint Arxiv:1409.1556.
XXV. Spanhol, Fabio A., Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. “A dataset for breast cancer histopathological image classification.” IEEE Transactions on Biomedical Engineering 63, no. 7 (2016): 1455-1462.
XXVI. Spanhol, Fabio Alexandre, Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. “Breast cancer histopathological image classification using convolutional neural networks.” In Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 2560-2567. IEEE, 2016.
XXVII. Spanhol, Fabio A., Luiz S. Oliveira, Paulo R. Cavalin, Caroline Petitjean, and Laurent Heutte. “Deep features for breast cancer histopathological image classification.” In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on, pp. 1868-1873. IEEE, 2017.
XXVIII. Tang, Y., 2013. Deep Learning Using Linear Support Vector Machines. Arxiv Preprint Arxiv: 1306.0239.
XXIX. T. Araujo, G. Aresta, E. Castro et al., “Classifcation of breast cancer histology images using convolutional neural networks,” PLoS ONE, vol. 12, no. 6, Article ID e0177544, 2017.
XXX. Veta, Mitko, Josien PW Pluim, Paul J. Van Diest, and Max A. Viergever. “Breast cancer histopathology image analysis: A review.” IEEE Transactions on Biomedical Engineering 61, no. 5 (2014): 1400-1411
XXXI. Yosinski, J., Clune, J., Bengio, Y. And Lipson, H., 2014. How Transferable Are Features In Deep Neural Networks?. In Advances In Neural Information Processing Systems (Pp. 3320–3328).
XXXII. Zeiler, M.D. And Fergus, R., 2014, September. Visualizing And Understanding Convolutional Networks. In European Conference On Computer Vision (Pp. 818–833). Springer, Cham.
XXXIII. Zhang, Yungang, Bailing Zhang, Frans Coenen, and Wenjin Lu. “Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles.” Machine vision and applications 24, no. 7 (2013): 1405-1420.

View Download