Abstract
The cancer that develops in the breast tissue is referred to as breast cancer. The cancer in the breast could be sometimes symptomatic and are identified by self-examination of the breast or by a physician, whereas in certain cases there could be no symptoms at all. However, signs like a lump in the breast, change of size of the breast, dimpling and fluid discharge from the nipple are some of the symptoms which are cause of grave concern. The early diagnosis of the disease is the key to combat the deadly disease paving the path for hope of life. Mammography is a very popular technique that is used for the early diagnosis of breast cancer. In this study, a technique for breast cancer classification in digitized mammogram is put-forth employing tetrolet transform based energy features and K-Nearest Neighbor (KNN) classifier. The breast mammogram images of benign and malignant category are decomposed into sub-band coefficients using tetrolet transform and the energy features are extracted. These extracted features are given as input to the KNN classifier. Results show better classification accuracy in the breast cancer images using tetrolet transform based energy features and KNN classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boudraa, S., Melouah, A., Merouani, H.F.: Deep texture representation for breast mass classification. In: 2018 International Conference on Signal, Image, Vision and their Applications (SIVA), pp. 1–4, IEEE (2018). https://doi.org/10.1109/siva.2018.8661052
Liu, B., Li, X., Li, J., Li, Y., Lang, J., Gu, R., Wang, F.: Comparison of machine learning classifiers for breast cancer diagnosis based on feature selection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4399–4404, IEEE (2018). https://doi.org/10.1109/smc.2018.00743
Alkhaleefah, M., Wu, C.C.: A hybrid CNN and RBF-based SVM approach for breast cancer classification in mammograms. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 894–899, IEEE (2018). https://doi.org/10.1109/smc.2018.00159
Chen, D., Qian, G., Pan, Q.: Breast cancer classification with electronic medical records using hierarchical attention bidirectional networks. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 983–988, IEEE (2018). https://doi.org/10.1109/bibm.2018.8621479
Soliman, O.O., Sweilam, N.H., Shawky, D.M.: Automatic breast cancer detection using digital thermal images. In: 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), pp. 110–113, IEEE (2018). https://doi.org/10.1109/cibec.2018.8641807
Elelimy, E., Mohamed, A.A.: Towards automatic classification of breast cancer histopathological image. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 299–306, IEEE (2018). https://doi.org/10.1109/icces.2018.8639219
Salem, M.A.M.: Mammogram-based cancer detection using deep convolutional neural networks. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 694–699, IEEE (2018). https://doi.org/10.1109/icces.2018.8639224
Kabir, S.M., Bhuiyan, M.I.H.: Classification of breast tumour in Contourlet transform domain. In: 2018 10th International Conference on Electrical and Computer Engineering (ICECE), pp. 289–292, IEEE (2018). https://doi.org/10.1109/icece.2018.8636769
Adeshina, S.A., Adedigba, A.P., Adeniyi, A.A., Aibinu, A.M.: Breast cancer histopathology image classification with deep convolutional neural networks. In: 2018 14th International Conference on Electronics Computer and Computation (ICECCO), pp. 206–212, IEEE (2018). https://doi.org/10.1109/icecco.2018.8634690
Angara, S., Robinson, M., Guillén-Rondon, P.: Convolutional neural networks for breast cancer histopathological image classification. In: 2018 4th International Conference on Big Data and Information Analytics (BigDIA), pp. 1–6, IEEE (2018). https://doi.org/10.1109/bigdia.2018.8632800
Dua, S., Acharya, U.R., Chowriappa, P., Sree, S.V.: Wavelet-based energy features for glaucomatous image classification. IEEE Trans. Inf. Technol. Biomed. 16(1), 80–87 (2012). https://doi.org/10.1109/TITB.2011.2176540
Ramesh, G.P., Malini, M., Professor, P.G.: An efficacious method of cup to disc ratio calculation for glaucoma diagnosis using super pixel. Int. J. Comput. Sci. Eng. Commun. 2(3) (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Amjath Ali, A., Mishra, S., Dappuri, B. (2020). Breast Cancer Classification Using Tetrolet Transform Based Energy Features and K-Nearest Neighbor Classifier. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-32644-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32643-2
Online ISBN: 978-3-030-32644-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)