Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques

M. Thangavel; Rahul Patnaik; Chandan Kumar Mishra; Smruti Ranjan Sahoo

doi:10.1007/978-981-16-9873-6_53

Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques

M. Thangavel
, Rahul Patnaik
, Chandan Kumar Mishra
, Smruti Ranjan Sahoo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.

Original language	English
Title of host publication	Intelligent and Cloud Computing - Proceedings of ICICC 2021
Editors	Debahuti Mishra, Rajkumar Buyya, Prasant Mohapatra, Srikanta Patnaik
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	581-593
Number of pages	13
ISBN (Print)	9789811698729
DOIs	https://doi.org/10.1007/978-981-16-9873-6_53
Publication status	Published - 2022
Externally published	Yes
Event	2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 - Bhubaneswar, India Duration: Oct 22 2021 → Oct 23 2021

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	286
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	2nd International Conference on Intelligent and Cloud Computing, ICICC 2021
Country/Territory	India
City	Bhubaneswar
Period	10/22/21 → 10/23/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast cancer
Convolutional neural network
Deep learning
Image processing
Machine learning

ASJC Scopus subject areas

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-16-9873-6_53

Cite this

Thangavel, M., Patnaik, R., Mishra, C. K., & Sahoo, S. R. (2022). Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. In D. Mishra, R. Buyya, P. Mohapatra, & S. Patnaik (Eds.), Intelligent and Cloud Computing - Proceedings of ICICC 2021 (pp. 581-593). (Smart Innovation, Systems and Technologies; Vol. 286). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-9873-6_53

Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. / Thangavel, M.; Patnaik, Rahul; Mishra, Chandan Kumar et al.
Intelligent and Cloud Computing - Proceedings of ICICC 2021. ed. / Debahuti Mishra; Rajkumar Buyya; Prasant Mohapatra; Srikanta Patnaik. Springer Science and Business Media Deutschland GmbH, 2022. p. 581-593 (Smart Innovation, Systems and Technologies; Vol. 286).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Thangavel, M, Patnaik, R, Mishra, CK & Sahoo, SR 2022, Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. in D Mishra, R Buyya, P Mohapatra & S Patnaik (eds), Intelligent and Cloud Computing - Proceedings of ICICC 2021. Smart Innovation, Systems and Technologies, vol. 286, Springer Science and Business Media Deutschland GmbH, pp. 581-593, 2nd International Conference on Intelligent and Cloud Computing, ICICC 2021, Bhubaneswar, India, 10/22/21. https://doi.org/10.1007/978-981-16-9873-6_53

Thangavel M, Patnaik R, Mishra CK, Sahoo SR. Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. In Mishra D, Buyya R, Mohapatra P, Patnaik S, editors, Intelligent and Cloud Computing - Proceedings of ICICC 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 581-593. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-16-9873-6_53

Thangavel, M. ; Patnaik, Rahul ; Mishra, Chandan Kumar et al. / Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques. Intelligent and Cloud Computing - Proceedings of ICICC 2021. editor / Debahuti Mishra ; Rajkumar Buyya ; Prasant Mohapatra ; Srikanta Patnaik. Springer Science and Business Media Deutschland GmbH, 2022. pp. 581-593 (Smart Innovation, Systems and Technologies).

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abstract = "With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Na{\"i}ve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.",

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PY - 2022

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N2 - With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.

AB - With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.

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Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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