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Machine Learning for Breast Cancer Diagnosis and Classification Using Hand-Crafted Features

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Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) (SoCPaR 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1383))

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Abstract

Analysis of biological processes (signals) is always probabilistic and statistical analysis. Machine Learning stemmed from advanced probabilistic and statistical analysis is the exact tool what the medical workforces are looking for. Research and studies in the area of Machine Learning algorithms enhances its accuracy; its healthcare applications in medical diagnosis can improve the treatment procedure. Healthcare application being directly related to human health, dimensionality reduction is very critical; it sometime triggers to panic losses; therefore, the complete feature involvement in the model development is challenging to enhance the reliability of the Machine Learning algorithm and accuracy of the algorithm. Depending upon the diagnosis area, Regression analysis leading to dimensionality reduction and Principal Component Analysis (PCA) to remove less significant features can be applied. Primarily, in cancer diagnosis, only noise removal is acceptable, not the feature with small significance removal and minimal human intervention is acceptable which could lead to reduced accuracy of diagnosis, so the following treatment procedure.

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References

  1. Mishra, V., Singh, Y., Rath, S.K.: Breast cancer detection from thermograms using feature extraction and machine learning techniques. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, pp. 1–5 (2019). https://doi.org/10.1109/I2CT45611.2019.9033713

  2. Wang, Z., et al.: Computer-aided diagnosis based on extreme learning machine: a review. IEEE Access 8, 141657–141673 (2020). https://doi.org/10.1109/ACCESS.2020.3012093

    Article  Google Scholar 

  3. Gupta, S., Sedmkar, R.: Machine learning for healthcare: introduction. In: Machine Learning with Health Care Perspective, Learning and Analytics in Intelligent Systems, vol. 13, Springer Nature Switzerland, pp. 1–25 (2020)

    Google Scholar 

  4. Rebala, G., Ravi, A., Churiwala, S.: Classification. In: An Introduction to Machine Learning, Springer Nature Switzerland, pp. 57–66 (2019)

    Google Scholar 

  5. Ritthipravat, P.: Artificial neural networks in cancer recurrence prediction. In: 2009 International Conference on Computer Engineering and Technology, Singapore, pp. 103–107 (2009). https://doi.org/10.1109/ICCET.2009.84

  6. Yassin, N.I., Omran, S., Houby, E.M., Allam, H.: Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput. Methods Programs Biomed. Elsevier 156, 25–45 (2018)

    Google Scholar 

  7. Oei, R.W., et al.: Convolutional Neural Network for cell classification using microscope image of intracellular actin networks. Plos One 14, e0213626 (2019)

    Google Scholar 

  8. Levin, A.B., Schlosser, C., Grewal, J., Coope, R., Jones, S., Yip, S.: Rise of the machines: advances in deep learning for cancer diagnosis. In: Trends in Cancer, Elsevier Inc, vol. 5, no. 3, pp. 157–169, March 2019

    Google Scholar 

  9. Vaka, A.R., Soni, B., Reddy, S.: Breast cancer detection by leveraging machine learning. In: The Korean Institute of Communications and Information Science (KICS), pp. 1–5 (2020)

    Google Scholar 

  10. Ak, M.F.: A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications. In: Healthcare MDPI (2020)

    Google Scholar 

  11. Smiti, A.: When machine learning meets medical world: current status and future challenges. In: Elsevier Inc. Comput. Sci. Rev. 37, 100280 (2020)

    Google Scholar 

  12. Shaikh, T.A., Ali, R.: A CAD tool for breast cancer prediction using Naive Bayes classifier. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 351–356 (2020). https://doi.org/10.1109/ESCI48226.2020.9167568

  13. Sengar, P.P., Gaikwad, M.J., Nagdive, A.S.: Comparative study of machine learning algorithms for breast cancer prediction. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, pp. 796–801 (2020). https://doi.org/10.1109/ICSSIT48917.2020.9214267

  14. Ivaturi, A., Singh, A., Gunanvitha, B., Chethan, K.S.: Soft classification techniques for breast cancer detection and classification. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, pp. 437–442 (2020). https://doi.org/10.1109/ICIEM48762.2020.9160219

  15. Laghmati,S., Tmiri, A., Cherradi, B.: machine learning based system for prediction of breast cancer severity. In: 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, pp. 1–5 (2019). https://doi.org/10.1109/WINCOM47513.2019.8942575

  16. Aldhaeebi, M., Bamatraf, S., Ramahi, O., Binajjaj, S.A.: breast tumor diagnosis using machine learning with microwave probes. In: 2019 First International Conference of Intelligent Computing and Engineering (ICOICE), Hadhramout, Yemen, pp. 1–4 (2019). https://doi.org/10.1109/ICOICE48418.2019.9035150

  17. Alghodhaifi, H., Alghodhaifi, A., Alghodhaifi, M.: Predicting invasive ductal carcinoma in breast histology images using convolutional neural network. In: 2019 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, pp. 374–378 (2019). https://doi.org/10.1109/NAECON46414.2019.9057822

  18. Brito-Sarracino, et al.: Explainable machine learning for breast cancer diagnosis. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil, pp. 681–686 (2019). https://doi.org/10.1109/BRACIS.2019.00124

  19. Murugan, S., Kumar, B.M., Amudha, S.: Classification and prediction of breast cancer using linear regression, decision tree and random forest. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, pp. 763–766 (2017). https://doi.org/10.1109/CTCEEC.2017.8455058

  20. Ray, S., AlGhamdi, A., AlGhamdi, A., Alshouiliy, K., Agrawal, D.P.: Selecting features for breast cancer analysis and prediction. In: 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE), Las Vegas, NV, USA, pp. 1–6 (2020). https://doi.org/10.1109/ICACCE49060.2020.9154919

  21. Dhanya, R., Paul, I.R., Akula, S.S., Sivakumar, M., Nair, J.J.: A comparative study for breast cancer prediction using machine learning and feature selection. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, pp. 1049–1055 (2019). https://doi.org/10.1109/ICCS45141.2019.9065563

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Author information

Authors and Affiliations

  1. Kathmandu Engineering College, Tribhuvan University, Kalimati, Kathmandu, Nepal

    Dhawa Sang Dong

  2. Hetauda School of Management and Social Science, Tribhuvan University, Kirtipur, Nepal

    Suraj Kandel

Authors
  1. Dhawa Sang Dong
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  2. Suraj Kandel
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Systems Innovation, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

    Yukio Ohsawa

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M.A. Jabbar

  5. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  6. Queen’s University, Belfast, UK

    Seán McLoone

  7. Northumbria University, Newcastle, UK

    Biju Issac

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Dong, D.S., Kandel, S. (2021). Machine Learning for Breast Cancer Diagnosis and Classification Using Hand-Crafted Features. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_35

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