Abstract
This paper discussed a framework for the detection of breast cancer cells by using various techniques. Dangerous cancer is mostly observed in women’s breast. The mortality rate can be decreased when breast cancer is detected at an early stage. By using different techniques, breast cancer cells can be detected. From the past decade, to detect and identify the stage of the cancer, computer-aided diagnosis (CAD) system has been initiated. This system consists of different steps like preprocessing, nuclei detection, segmentation, feature extraction, and classification to detect breast cancer cells. The approaches and methodologies in each step of the CAD system are applied to the images for cancer cell detection. Classification is done by using different classifiers. Features and classification results of different techniques for various images for detecting breast cancer cells are reviewed.
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
Similar content being viewed by others
References
Abdallah, Y.M., Elgak, S., Zain, H., Rafiq, M., Ebaid, E.A., Elnaema, A.A.: Breast cancer detection using image enhancement and segmentation algorithms. Biomed. Res. 29(20), 3732–3736 (2018)
Angayarkanni, N., Kumar, D., Arunachalam, G.: The application of image processing techniques for detection and classification of cancerous tissue in digital mammograms. J. Pharm. Sci. Res. 8(10), 1179 (2016)
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6), e0177, 544 (2017)
Arpana, M., Kiran, P.: Feature extraction values for digital mammograms. Int. J. Soft Comput. Eng. (IJSCE) 4(2), 183–187 (2014)
Badawy, S.M., Hefnawy, A.A., Zidan, H.E., GadAllah, M.T.: Breast cancer detection with mammogram segmentation: a qualitative study. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(10) (2017)
Bagchi, S., Huong, A.: Signal processing techniques and computer-aided detection systems for diagnosis of breast cancer—a review paper (2017)
Biswas, S.K., Mia, M.M.A.: Image reconstruction using multi layer perceptron (mlp) and support vector machine (svm) classifier and study of classification accuracy. Int. J. Sci. Technol. Res. 4(2), 226–231 (2015)
Chan, A., Tuszynski, J.A.: Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R. Soc. Open Sci. 3(12), 160,558 (2016)
Dhas, A.S., Vijikala, V.: An improved cad system for abnormal mammogram image classification using svm with linear kernel (2017)
Durgesh, K.S., Lekha, B.: Data classification using support vector machine. J. Theor. Appl. Inf. Technol. 12(1), 1–7 (2010)
George, Y.M., Zayed, H.H., Roushdy, M.I., Elbagoury, B.M.: Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst. J. 8(3), 949–964 (2013)
Ghongade, R., Wakde, D.: Computer-aided diagnosis system for breast cancer using rf classifier, pp. 1068–1072 (2017)
Jadoon, M.M., Zhang, Q., Haq, I.U., Butt, S., Jadoon, A.: Three-class mammogram classification based on descriptive cnn features. BioMed Res. Int. (2017)
Jagdale, S., Kolekara, M.H., Khot, U.P.: Smart sensing using bayesian network for computer aided diagnostic systems. Proc. Comp. Sci. 45, 762–769 (2015)
Jalalian, A., Mashohor, S., Mahmud, R., Karasfi, B., Saripan, M.I.B., Ramli, A.R.B.: Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection, p. 113. Leibniz Research Centre for Working Environment and Human Factors (2017)
Kaur, P., Singh, G., Kaur, P.: Intellectual detection and validation of automated mammogram breast cancer images by multi-class svm using deep learning classification. Inform. Med. Unlock. 100151 (2019)
Kaymak, S., Helwan, A., Uzun, D.: Breast cancer image classification using artificial neural networks. Proc. Comput. Sci. 120, 126–131 (2017)
Kowal, M., Filipczuk, P.: Nuclei segmentation for computer-aided diagnosis of breast cancer. Int. J. Appl. Math. Comput. Sci. 24(1), 19–31 (2014)
Kumar, R., Srivastava, R., Srivastava, S.: Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J. Med. Eng. (2015)
Maitra, I.K., Bandyopadhyay, S.K.: Cad based method for detection of breast cancer (2018)
McDonald, E.S., Clark, A.S., Tchou, J., Zhang, P., Freedman, G.M.: Clinical diagnosis and management of breast cancer. J Nucl Med 57(Suppl 1), 9S–16S (2016)
Ragab, D.A., Sharkas, M., Marshall, S., Ren, J.: Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7, e6201 (2019)
Rajyalakshmi, U., Rao, S.K., Prasad, K.S.: Supervised classification of breast cancer malignancy using integrated modified marker controlled watershed approach, pp. 584–589 (2017)
Rao, S.K.: Integrated novel multi-phase level sets with modified marker controlled watershed for segmentation of breast cancer histopathological images (2017)
Saha, M., Mukherjee, R., Chakraborty, C.: Computer-aided diagnosis of breast cancer using cytological images: a systematic review. Tissue Cell 48(5), 461–474 (2016)
Saraswathi, D., Srinivasan, E., Ranjitha, P.: Extreme learning machine for cancer classification in mammograms based on fractal and glcm features. Asian J. Appl. Sci. Technol. (AJAST) 1(4), 1–6 (2017)
Singh, A.K., Gupta, B.: A novel approach for breast cancer detection and segmentation in a mammogram. Proc. Comput. Sci. 54, 676–682 (2015)
Uppada, R., Sanagapallela, K.R., Kodati, S.P.: Image automatic categorisation using selected features attained from integrated non-subsampled contourlet with multiphase level sets, pp. 67–75 (2019)
Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., Li, L.: Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6, 27,327 (2016)
Win, K.Y., Choomchuay, S., Hamamoto, K., Raveesunthornkiat, M., Rangsirattanakul, L., Pongsawat, S.: Computer aided diagnosis system for detection of cancer cells on cytological pleural effusion images. BioMed Res. Int. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kandubothula, V., Uppada, R., Nandan, D. (2020). A Review on Detection of Breast Cancer Cells by Using Various Techniques. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_73
Download citation
DOI: https://doi.org/10.1007/978-981-15-4032-5_73
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4031-8
Online ISBN: 978-981-15-4032-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)