Breast Tumor Detection Using Classification Algorithm

Authors

  • Mitali Department of CSE, Amity University Haryana, India Author
  • Aman Jatain Assistant Professor, Amity University Haryana, India Author
  • Swati Gupta Assistant Professor, Amity University Haryana, India Author

Keywords:

Malignant, Benign, Diagnosis, Radiologist, Survival, Tumors

Abstract

Breast cancer is the most common cancer  in women. It is the leading cause of cancer death in  developing countries and the second leading cause of  cancer death in women in the United States, behind only  lung cancer. Females are more likely to develop breast  cancer. However, in a few instances, it is clear that males  have also been affected. Breast cancer has been  discovered. [1] Breast tumors may be either cancerous or non- cancerous.  Benign tumors are easily treated with doctor-prescribed  medications. Malignant tumors are a sign of a high risk of  breast cancer and should be removed as soon as possible.  Early identification and treatment of tumor lowers the  risk of cancer-related death. The survival rate of breast  cancer patients in America has been reported to be 90%  in recent years, but it is as low as 60% in India. [2]  The key cause of this shortcoming is late tumor diagnosis  and classification, which causes treatment delays. Using  machine learning to perform a task will reduce the  workload of physicians and radiologists. According to  research, doctors can only diagnose breast cancer with a  79 percent accuracy rate, while machines can diagnose it  with a 91 percent accuracy rate. Early diagnosis is the  best way to increase the chance of treatment and chance  of survival.

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References

J. E. T. Akinsola, S. O. Kuyoro, O. Awodele& F. A. Kasali, Performance Evaluation of Supervised Machine Learning Algorithms Using Multi-CriteriaDecision Making Techniques. International Conference on Information Technology in Education and Development (ITED) Proceedings, 17 – 34, 2019.

Abdullah-Al Nahid, Aaron Mikaelian and Yinan Kong, "Histopathological breast-image classification with restricted Boltzmann machine along with backpropagation", Biomedical Research, vol. 29, no. 10, 2018.

Mohamad Mahmoud Al Rahhal, "Breast Cancer Classification in Histopathological Images using Convolutional Neural Network", International Journal of Advanced ComputerScience and Applications (IJACSA), vol. 9, no.3, 2018.

Vikas Chaurasia, Saurabh Pal and BB Tiwari, "Prediction of benign and malignant breast cancer using data mining techniques", Journal of Algorithms & Computational

Technology 2018, vol. 12, no. 2, pp. 119-126,2018. [5] et al., "A parallel random forest algorithm for big data in a Spark cloud computing environment", IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 919-933, 2017.

Chetak Kandaswamy et al., "High-content analysis of breast cancer using single-cell deep transfer learning", Journal of biomolecular screening, vol. 21, no. 3, pp. 252- 259, 2016.

Pedro Henriques Abreu et al., "Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review", ACM Computing Surveys (CSUR), vol. 49, no.3, 2016.

T Choudhury, V Kumar, D Nigam and B Mandal, "Intelligent classification of lung & oral cancer through diverse data mining algorithms", International Conference on Micro-Electronics and Telecommunication Engineering, 2016.

M. H. Yap et al., "Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks", IEEE Journalof Biomedical and Health Informatics, vol. 22, no. 4, pp. 1218-1226, July 2018.

Silva, J., Lezama, O.B.P., Varela, N., Borrero, L.A.: Integration of data mining classification techniques and ensemble learningfor predicting the type of breast cancer recurrence. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds.) GPC 2019. LNCS, vol. 11484, pp. 18–30. Springer, Cham (2019).

Ojha U., Goel, S.: A study on prediction ofbreast cancer recurrence using data mining techniques. In: 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, IEEE, pp. 527–530, 2017.

Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique. In: 19th International Conference on Computer and Information Technology (ICCIT), pp. 310–314. IEEE (2016).

Asri, H., Mousannif, H., Al, M.H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Sci. 83, 1064–1069 (2016)

Darrab, S., Ergenc, B., Vertical pattern mining algorithm for multiple support thresholds. In: International Conference on Knowledge Based and Intelligent Information and Engineering (KES), Procedia Computer Science, vol. 112, pp. 417–426 (2017).

Mohammed S.A., Darrab S., Noaman S.A.,Saake G. (2020) Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan Y., Shi Y., Tuba M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore

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Published

2021-05-30

Issue

Vol. 9 No. 3 (2021): International Journal of Innovative Research in Computer Science and Technology (May) 2021

Section

Articles

How to Cite

Breast Tumor Detection Using Classification Algorithm . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(3), 27–30. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11480