Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning

doi:10.23940/ijpe.23.09.p6.607623

Abstract

Abstract: One of the significant contributors to the present death rate for women is breast cancer. An earlier diagnosis of this illness can save costs and increase survival rates. In the past, machine learning algorithms that were used to categorize data were only dependent on manually created features, which could not adequately capture variations and needed a higher degree of classification accuracy. The classification problem was also solved using deep learning methods. The experiment seeks to categorize the cancerous cell types — benign and malignant - using a convolutional neural network (CNN) based model. It is the most incredible technique for classifying images since it is one of the best at identifying the items and patterns in an image. It is a that have a kind of deep learning approach that is best known for spotting patterns in images; it aims to present images in an abstract form that contains the most soundless information required for differentiating them from other images with a similar appearance. BreakHis, a dataset with microscopic biopsy images, and BUSI, a dataset with histopathology images, were employed for this investigation. The primary goal of this experiment is to experiment with the model used to diagnose breast cancer by categorizing various breast photos as either cancerous (Malignant) or non-cancerous (Benign).

Key words: neural networks, patch-based classification, deep learning, transfer learning, breast cancer

Aashita Rajput, Muskan Yadav, Sachin Yadav, Megha Chhabra, and Arun Prakash Agarwal. Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning [J]. Int J Performability Eng, 2023, 19(9): 607-623.

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References

1. Roslidar R., Saddami K., Arnia F., Syukri M., andMunadi K.A Study of Fine-Tuning CNN Models based on Thermal Imaging for Breast Cancer Classification. In2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE, pp. 77-81, 2019.
2. Heenaye-Mamode Khan, M., Boodoo-Jahangeer, N., Dullull, W., Nathire, S., Gao, X., Sinha, G.R., and Nagwanshi, K.K. Multi-Class Classification of Breast Cancer Abnormalities using Deep Convolutional Neural Network (CNN). Plos one, vol. 16, no. 8, pp. 0256500, 2021.
3. Zhang Y.D., Satapathy S.C., Guttery D.S., Górriz J.M., andWang S.H.Improved Breast Cancer Classification through Combining Graph Convolutional Network and Convolutional Neural Network. Information Processing & Management, vol. 58, no. 2, pp. 102439, 2021.
4. Albashish D.,Al-Sayyed, R., Abdullah, A., Ryalat, M.H., and Almansour, N.A. Deep CNN Model based on VGG16 for Breast Cancer Classification. In2021 International conference on information technology (ICIT), IEEE, pp. 805-810, 2021.
5. Benhammou Y., Tabik S., Achchab B., andHerrera F.A First Study Exploring the Performance of the State-of-The Art CNN Model in the Problem of Breast Cancer. Inproceedings of the international conference on learning and optimization algorithms: theory and applications, pp. 1-6, 2018.
6. Gour M., Jain S., andSunil Kumar, T. Residual Learning Based CNN for Breast Cancer Histopathological Image Classification. International Journal of Imaging Systems and Technology, vol. 30, no. 3, pp. 621-635, 2020.
7. Gao F., Wu T., Li J., Zheng B., Ruan L., Shang D., andPatel B.SD-CNN: A Shallow-Deep CNN for Improved Breast Cancer Diagnosis. Computerized Medical Imaging and Graphics, vol. 70, pp. 53-62, 2018.
8. Chiao J.Y., Chen K.Y., Liao K.Y.K., Hsieh, P.H., Zhang, G., and Huang, T.C. Detection and Classification the Breast Tumors using Mask R-CNN on Sonograms. Medicine, vol. 98, no. 19, 2019.
9. Choudhary V., Guha P., Pau G., Dhanaraj R.K., andMishra S.Automatic Classification of Cowpea Leaves using Deep Convolutional Neural Network. Smart Agricultural Technology, vol. 4, pp. 100209, 2023.
10. Kumar A., Vishwakarma A., andBajaj V.Crccn-Net: Automated Framework for Classification of Colorectal Tissue using Histopathological Images. Biomedical Signal Processing and Control, vol. 79, pp. 104172, 2023.
11. Mewada H.K., Patel A.V., Hassaballah M., Alkinani M.H., andMahant K.Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification. Sensors, vol. 20, no. 17, pp. 4747, 2020.
12. Sarvamangala, D.R. and Kulkarni, R.V.Convolutional Neural Networks in Medical Image Understanding: A Survey. Evolutionary intelligence, vol. 15, no. 1, pp. 1-22, 2022.
13. Zou Y., Zhang J., Huang S., andLiu B.Breast Cancer Histopathological Image Classification using Attention High‐Order Deep Network. International Journal of Imaging Systems and Technology, vol. 32, no. 1, pp.266-279, 2022.
14. Nair N.B., Singh T., Thakur A., andDuraisamy P.Deployment of Breast Cancer Hybrid Net using Deep Learning. In2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, pp. 1-6, 2022.
15. Jadah Z., Alfitouri A., Chantar H., Amarif M., andAeshah A.A.Breast Cancer Image Classification using Deep Convolutional Neural Networks. In2022 International Conference on Engineering & MIS (ICEMIS), IEEE, pp. 1-6, 2022.
16. Afaq, S. and Jain, A.MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images. In2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), IEEE, pp. 177-182, 2022.
17. Tewari Y., Ujjwal E., andKumar L.Breast Cancer Classification using Machine Learning. In2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, pp. 01-04, 2022.
18. Bhise S., Gadekar S., Gaur A.S., Bepari S., Kale D., andAswale S.Detection of Breast Cancer using Machine Learning and Deep Learning Methods. In2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE, pp. 1-6, 2022.
19. Juneja, K. and Rana, C.An Improved Weighted Decision Tree Approach for Breast Cancer Prediction. International Journal of Information Technology, vol. 12, no. 3, pp. 797-804, 2020.
20. Ma D., Shang L., Tang J., Bao Y., Fu J., andYin J.Classifying Breast Cancer Tissue by Raman Spectroscopy with One-Dimensional Convolutional Neural Network. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 256, pp. 119732, 2021.
21. Samee N.A., Atteia G., Meshoul S., Al-antari, M.A., and Kadah, Y.M. Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach. Mathematics, vol. 10, no. 19, pp. 3631, 2022.
22. Liu K., Kang G., Zhang N., andHou B.Breast Cancer Classification based on Fully-Connected Layer First Convolutional Neural Networks. IEEE Access, vol. 6, pp. 23722-23732, 2018.
23. Nguyen P.T., Nguyen T.T., Nguyen N.C., andLe T.T.Multiclass Breast Cancer Classification using Convolutional Neural Network. In2019 International symposium on electrical and electronics engineering (ISEE), IEEE, pp. 130-134, 2019.
24. Hijab A., Rushdi M.A., Gomaa M.M., andEldeib A.Breast Cancer Classification in Ultrasound Images using Transfer Learning. In2019 Fifth international conference on advances in biomedical engineering (ICABME), IEEE, pp. 1-4, 2019.
25. Liang G., Wang X., Zhang Y., Xing X., Blanton H., Salem T., andJacobs N.Joint 2d-3d Breast Cancer Classification. In2019 IEEE International Conference on Bioinformatics and biomedicine (BIBM), IEEE, pp. 692-696, 2019.
26. Nawaz M., Sewissy A.A., andSoliman T.H.A. Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network. International Journal of Advanced Computer Science and Applications, vol. 9, no. 6, pp. 316-332, 2018.
27. Wang Y., Sun L., Ma K., andFang J.Breast Cancer Microscope Image Classification based on CNN with Image Deformation. In Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27-29, 2018, Proceedings 15, Springer International Publishing, pp. 845-852, 2018.
28. Roslidar R., Rahman A., Muharar R., Syahputra M.R., Arnia F., Syukri M., Pradhan B., andMunadi K.A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection. IEEE Access, vol. 8, pp. 116176-116194, 2020.
29. Davoudi, K. and Thulasiraman, P.Evolving Convolutional Neural Network Parameters through the Genetic Algorithm for the Breast Cancer Classification Problem. Simulation, vol. 97, no. 8, pp. 511-527, 2021.
30. Ibraheem A.M., Rahouma K.H., andHamed H.F.3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification through Histopathological Images. Journal of Medical and Biological Engineering, vol. 41, no. 4, pp. 494-503, 2021.
31. Araújo T., Aresta G., Castro E., Rouco J., Aguiar P., Eloy C., Polónia A., andCampilho A.Classification of Breast Cancer Histology Images using Convolutional Neural Networks. PloS one, vol. 12, no. 6, pp. 0177544, 2017.
32. Wang Y., Choi E.J., Choi Y., Zhang H., Jin G.Y., andKo S.B.Breast Cancer Classification in Automated Breast Ultrasound using Multiview Convolutional Neural Network with Transfer Learning. Ultrasound in medicine & biology, vol. 46, no. 5, pp. 1119-1132, 2020.
33. Sharma S., Mehra R., andKumar S.Optimised CNN in Conjunction with Efficient Pooling Strategy for the Multi‐Classification of Breast Cancer. IET Image Processing, vol. 15, no. 4, pp. 936-946, 2021.
34. Umer M.J., Sharif M., andWang S.H.Breast Cancer Classification and Segmentation Framework using Multiscale CNN and U‐Shaped Dual Decoded Attention Network.Expert Systems, pp. 13192, 2022.
35. Abdelrahman L.,Al Ghamdi, M., Collado-Mesa, F., and Abdel-Mottaleb, M. Convolutional Neural Networks for Breast Cancer Detection in Mammography: A Survey. Computers in biology and medicine, vol. 131, pp. 104248, 2021.
36. Yao H., Zhang X., Zhou X., andLiu S.Parallel Structure Deep Neural Network using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers, vol. 11, no. 12, pp. 1901, 2019.
37. Wang Z., Li M., Wang H., Jiang H., Yao Y., Zhang H., andXin J.Breast Cancer Detection using Extreme Learning Machine based on Feature Fusion with CNN Deep Features. IEEE Access, vol. 7, pp. 105146-105158, 2019.
38. Wang X., Ahmad I., Javeed D., Zaidi S.A., Alotaibi F.M., Ghoneim M.E., Daradkeh Y.I., Asghar J., andEldin E.T.Intelligent Hybrid Deep Learning Model for Breast Cancer Detection. Electronics, vol. 11, no. 17, pp. 2767, 2022.

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