Classification of Mammogram Images Using Radial Basis Function Neural Network
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 us...
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| Main Authors: | , , , , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer Nature Switzerland
2020
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/27783/1/Classification%20of%20Mammogram%20Images%20Using%20Radial%20Basis%20Function%20Neural%20Network%20-%20Copy.pdf http://ir.unimas.my/id/eprint/27783/ https://link.springer.com/chapter/10.1007/978-3-030-33582-3_30 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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. |
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