Classification of Mammogram Images Using Radial Basis Function Neural Network

Ashraf Osman, Bin Ibrahim and Ali, Ahmed and Aleya, Abdu and Rahma, Abd-alaziz and Mohamed Alhaj, Alobeed and Abdulrazak Yahya, Saleh Al-Hababi and Abubakar, Elsafi (2020) Classification of Mammogram Images Using Radial Basis Function Neural Network. In: Emerging Trends in Intelligent Computing and Informatics : Data Science, Intelligent Information Systems and Smart Computing. Advances in Intelligent Systems and Computing, 1073 . Springer Nature Switzerland, pp. 311-320. ISBN 978-3-030-33582-3

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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.

Item Type: Book Chapter
Uncontrolled Keywords: Artificial Neural Networks � Radial Basis Function � Mammogram � Breast Cancer, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Gani
Date Deposited: 13 Nov 2019 00:58
Last Modified: 29 Jun 2020 02:09

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