Predicting and Classifying Breast Cancer Using Machine Learning.

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  • Author(s): Alkhathlan L;Alkhathlan L; Saudagar AKJ; Saudagar AKJ
  • Source:
    Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2022 Jun; Vol. 29 (6), pp. 497-514. Date of Electronic Publication: 2021 Dec 09.
  • Publication Type:
    Journal Article; Research Support, Non-U.S. Gov't
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 9433358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8666 (Electronic) Linking ISSN: 10665277 NLM ISO Abbreviation: J Comput Biol Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Mary Ann Liebert, Inc., c1994-
    • Subject Terms:
    • Abstract:
      The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.
    • Contributed Indexing:
      Keywords: breast cancer; classification; deep learning; machine learning; prediction
    • Publication Date:
      Date Created: 20211209 Date Completed: 20220609 Latest Revision: 20220824
    • Publication Date:
      20240105
    • Accession Number:
      10.1089/cmb.2021.0236
    • Accession Number:
      34883032