Classification of Mammogram Images Using Radial Basis Function Neural Network

  • Ashraf Osman IbrahimEmail author
  • Ali Ahmed
  • Aleya Abdu
  • Rahma Abd-alaziz
  • Mohamed Alhaj Alobeed
  • Abdulrazak Yahya Saleh
  • Abubakar Elsafi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


Recently, computer aided diagnosis and image processing have received considerable attention from a number of researchers. Mammography is the most effective method for exposure of early breast cancer to increase the survival rate. This paper presents the classification method for mammogram Image using Radial Basis Function Network (RBF) technique. This method is focused on features extracted from the breast cancer mammogram image processing algorithms. The actual decision about the presence of the disease is then made by RBF network classifiers. We conducted this study in five stages; collecting images, Region of Interest (ROI), features extracting, classification and end with testing and evaluating. The experimental results shown the classification accuracy results of the RBF neural network 79.166% while MLP algorithm was 54.1667%, that illustrate the capability of the RBF network to obtain better classification accuracy results.


Artificial Neural Networks Radial Basis Function Mammogram Breast Cancer 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ashraf Osman Ibrahim
    • 1
    • 2
    • 3
    Email author
  • Ali Ahmed
    • 4
  • Aleya Abdu
    • 3
  • Rahma Abd-alaziz
    • 3
  • Mohamed Alhaj Alobeed
    • 5
  • Abdulrazak Yahya Saleh
    • 6
  • Abubakar Elsafi
    • 7
  1. 1.Faculty of Computer Science and Information TechnologyAlzaiem Alazhari UniversityKhartoum NorthSudan
  2. 2.Arab Open UniversityKhartoumSudan
  3. 3.Faculty of Computer ScienceFuture UniversityKhartoumSudan
  4. 4.Department of Computer Science, Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabighSaudi Arabia
  5. 5.Information Technology DepartmentShendi UniversityShendiSudan
  6. 6.FSKPM FacultyUniversity Malaysia Sarawak (UNIMAS)Kota SamarahanMalaysia
  7. 7.Software Engineering Department, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

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