A Breast Cancer Diagnosis Framework Based on Machine Learning

Authors

  • Dr. Nikhat Akhtar  Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Dr. Hemlata Pant  Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Apoorva Dwivedi  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Vivek Jain  Professor, Department of Computer Science & Engineering, IMS Engineering College, Ghaziabad, Uttar Pradesh, India
  • Dr. Yusuf Perwej  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET2310375

Keywords:

Machine Learning, Breast Cancer, Malignant, Classification, Convolutional Neural Network (CNN), Wisconsin Breast Cancer Datasets (WBCD), UCI Machine Learning Repository.

Abstract

Breast cancer is becoming the leading cause of mortality among women. One of the most prevalent diseases in women, breast cancer is brought on by a variety of clinical, lifestyle, social, and economic variables. Predictive approaches based on machine learning offer methods for diagnosing breast cancer sooner. It may be found using a variety of analytical methods, including Breast MRI, X-ray, thermography, mammograms, ultrasound, etc. The most prevalent technique for performance evaluation uses accuracy measures, and the Convolutional Neural Network (CNN) is the most accurate and widely used model for breast cancer diagnosis. The Wisconsin Breast Cancer Datasets (WBCD) were used to evaluate the suggested method. Out of a total of 569 samples, 273 samples were chosen for this experiment as the test data, while the other samples were utilized for training and validation. The review's findings showed that the Convolutional Neural Network (CNN) is the most effective and widely used model for finding breast cancer, and that the most often used technique for judging performance is accuracy metrics. The application of deep learning to such a wide range of real-world issues is astounding.

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2023-06-30

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Volume 10, Issue 3, May-June-2023

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[1]
Dr. Nikhat Akhtar, Dr. Hemlata Pant, Apoorva Dwivedi, Vivek Jain, Dr. Yusuf Perwej "A Breast Cancer Diagnosis Framework Based on Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 3, pp.118-132, May-June-2023. Available at doi : https://doi.org/10.32628/IJSRSET2310375