Breast Cancer Image Analysis and Classification Framework by Applying Machine Learning Techniques
Keywords:
Breast cancer, Machine Learning, Prediction, Wisconsin DiagnosisAbstract
Breast cancer is the most well-known kind of malignant growth among Indian ladies. One out of every two Indian ladies determined to have been diagnosed with breast cancer dies, bringing about a half opportunity of death. It is one of the essential research topics since many women died due to a lack of awareness. It could be better to detect it early to save many women's lives. The motivation behind this work is to look at broadly involved AI techniques for breast cancer prediction. The Wisconsin Diagnosis Breast Cancer informational index is utilized to carry out the paper and to analyse the exhibition of a few AI approaches regarding exactness. The results are severe and can be utilized for both discovery and treatment. If we can find the cancer at its early stages, we have developed a cure for it and study the patterns of the disease to find the genetics it produces. We can reduce the usage of various diagnostic tests by collecting blood samples and scanning for cells by machine learning techniques.
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B.M. Gayathri, Dr. C.P. Sumathi, “Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer” 2016.
S Kharya and S Soni,” Weighted Naïve Bayes classifier –Predictive model for breast cancer detection”, January 2016.
Mohd, F.,Thomism, “Comparison of different classification techniques using WEKA for Breast cancer” 2017.
Y. khoudfi and M. Bahaj, Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification, 978-1-5386- 4225-2/18/$31.00 ©2018 IEEE.
Tripathi, M.K. and Maktedar, D.D., 2020. A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey. Information Processing in Agriculture, 7(2), pp.183-203.
H. Osman, “An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 4, pp. 158–165, 2017.
Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565-1567. doi:10.1038/nbt1206-1565.
Larose DT. Discovering Knowledge in Data. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2004.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York, NY: Springer-Verlag;2001.
Yang, P., Wu, W., Wu, C., Shih, Y., Hsieh, C. & Hsu, J. (2021). Breast cancer recurrence prediction with ensemble methods and costsensitive learning. Open Medicine, 16(1), 754-768. https://doi.org/10.1515/med-2021-0282
Tripathi, M.K. and Maktedar, D.D., 2018. A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation. International Journal of Computer Applications, 975, p.8887.
Zahra Nematzadeh, Roliana Ibrahim and Ali Selamat, (2015). Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques, Proc. in 2015 10th Asian Control Conf. (ASCC), pp 1-6, IEEE.
Magboo, M. S. A., & Coronel, A. D. (2019). 30-Day Hospital Readmission Prediction Model for Diabetic Patients within the 30-70
Age Group. Proceedings of the Academics World 130th International Conference, Madrid, Spain, 10th - 11th June, 2019,8.https://www.worldresearchlibrary.org/up_proc/pdf/296815656902101-8.pdf
Tripathi, Mukesh Kumar, and Dhananjay D. Maktedar. "Internal quality assessment of mango fruit: an automated grading system with ensemble classifier." The Imaging Science Journal 70, no. 4 (2022): 253-272.upt
Landis, J., & Koch, G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159-174. doi:10.2307/2529310
Zain, Zuhaira Muhammad et al. (2020). Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis. International Journal of Advances in Intelligent Informatics, [S.l.], v. 6, n. 3, p. 313-327. ISSN 2548-3161.
Tripathi, M.K. and Maktedar, D.D., 2016, August. Recent machine learning based approaches for disease detection and classification of agricultural products. In 2016 International Conference on Computing Communication Control and automation (ICCUBEA) (pp. 1-6). IEEE.
Desuky, A.S., Hussain, S. (2021) An Improved Hybrid Approach for Handling Class Imbalance Problem. Arab J Sci Eng 46, 3853– 3864. https://doi.org/10.1007/s13369-021-05347-7
Ahmad L.G., Eshlaghy A.T., Poorebrahimi A., Ebrahimi M., Razavi A.R. (2013). Using three machine learning techniques for predicting breast cancer recurrence. Journal of Health & Medical Informatics. 4(2): 124.
Huang M-W, Chen C-W, Lin W-C, Ke S-W, Tsai C-F (2017) SVM and SVM Ensembles in Breast Cancer Prediction. PLoS ONE 12(1): e0161501. https://doi.org/10.1371/journal.pone.0161501.
Tripathi, Mukesh Kumar, and Dhananjay D. Maktedar. "Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm." IET Image Processing 15, no. 9 (2021): 1940-1956.
Zhou X, Li C, Rahaman MM, Yao Y, Ai S, Sun C, et al. A comprehensive review for breast histopathology image analysis using classical and deep neural networks. IEEE Access 2020;8:90931–56.
Sutanto DH, Ghani MKA. A benchmark of classification framework for noncommunicable disease prediction: a review. 2015.
Chiranjeevi, K., Tripathi, M.K. and Maktedar, D.D., 2021, March. Block chain Technology in Agriculture Product Supply Chain. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 1325-1329). IEEE.
Mahmood M, Al-Khateeb B, Alwash WM. A review on neural networks approach on classifying cancers. IAES Int J Artif Intell 2020;9:317–26. https://doi.org/ 10.11591/ijai.v9.i2.pp317-326.
Fatima N, Liu L, Hong S, Ahmed H. Prediction of breast cancer, comparative review of machine learning techniques, and their analysis. IEEE Access 2020;8: 150360–76. https://doi.org/10.1109/ACCESS.2020.3016715.
Channapattana, Shylesha V., Srinidhi Campli, A. Madhusudhan, Srihari Notla, Rachayya Arkerimath, and Mukesh Kumar Tripathi. "Energy analysis of DI-CI engine with nickel oxide nanoparticle added azadirachta indica biofuel at different static injection timing based on exergy." Energy 267 (2023): 126622.
Pang T, Wong JHD, Ng WL, Chan CS. Deep learning radiomics in breast cancer with different modalities: overview and future. Expert Syst Appl 2020;158:113501. https://doi.org/10.1016/j.eswa.2020.113501.
Punitha S, Amuthan A, Joseph KS. Enhanced monarchy butterfly optimization technique for effective breast cancer diagnosis. J Med Syst 2019;43:206. https:// doi.org/10.1007/s10916-019-1348-8.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6): 394–424. https://doi.org/10.3322/caac.21492
Fitzmaurice, C., Akinyemiju, T. F., Al Lami, F. H., Alam, T., Alizadeh-Navaei, R., Allen, C., ... & Yonemoto, N. (2018) Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2016: a systematic analysis for the global burden of disease study. JAMA oncology, 4(11): 1553-1568.
Rosa Mendoza, E. S., Moreno, E., & Caguioa, P. B. (2013) Predictors of early distant metastasis in women with breast cancer. Journal of cancer research and clinical oncology, 139(4): 645–652. https://doi.org/10.1007/s00432-012-1367-z.
Riggio, A.I., Varley, K.E. & Welm, A.L. (2021) The lingering mysteries of metastatic recurrence in breast cancer. Br J Cancer 124: 13–26. https://doi.org/10.1038/s41416-020-01161-4.
I. Pritom, M. A. R. Munshi, S. A. Sabab and S. Shihab. (2016) Predicting breast cancer recurrence using effective classification and feature selection technique. 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 310-314.
David A. Omondiagbe et al 2019 IOP Conf. Ser.: Mater. Sci. Eng. 495 012033.
Bian, K., Zhou, M., Hu, F., & Lai, W. (2020). RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction. Frontiers in genetics, 11, 566057. https://doi.org/10.3389/fgene.2020.566057
Quinlan JR. C4.5: Programs for Machine Learning.; 2014:302.https://books.google.com/books?hl=fr&lr=&id=b3ujBQAAQBAJ&pgis=1.
L. Latchoumi, T. P., & Parthiban, “Abnormality detection using weighed particle swarm optimization and smooth support vector machine,” Biomed. Res., vol. 28, no. 11, pp. 4749–4751, 2017.
Shivendra, Chiranjeevi, K., & Tripathi, M. K. (2022). Detection of Fruits Image Applying Decision Tree Classifier Techniques. In Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022 (pp. 127-139). Singapore: Springer Nature Singapore.
Fabian Pedregosa and all (2020). "Scikit-learn: Machine Learning in Python". Journal of Machine Learning Research. 12: 2825–2830.
Gautam R, Kaur P, Sharma M. A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings. Prog Artif Intell 2019; 8: 401–24. https://doi.org/10.1007/s13748-019-00191-1.
Tripathi, M.K. and Maktedar, D.D., 2021. Detection of various categories of fruits and vegetables through various descriptors using machine learning techniques. International Journal of Computational Intelligence Studies, 10(1), pp.36-73.
Sashank, Y. T. ., Kakulapati, V. ., & Bhutada, S. . (2023). Student Engagement Prediction in Online Session. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 43–47. https://doi.org/10.17762/ijritcc.v11i2.6108
Castro, J., González, J., Rodríguez, C., Garcia, J., & Jóhann, Þorvaldsson. Predicting Student Dropout Rates in Engineering Programs with Machine Learning. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/105
Dhabliya, D., & Sharma, R. (2019). Cloud computing based mobile devices for distributed computing. International Journal of Control and Automation, 12(6 Special Issue), 1-4. doi:10.33832/ijca.2019.12.6.01
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