Klasifikasi Sel-Sel Kanser Payudara Menggunakan Rangkaian Neural Peta-Peta Penubuhan-Diri (Som)
The rapid era of technology advancement has contributed a big impact against science and technology approach. This development achievement in medical investigation could helps to detect cancer infection which expanded mostly among the women. Cancer detection in its earliest stage definitely is...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2006
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| Subjects: | |
| Online Access: | http://eprints.usm.my/58731/1/Klasifikasi%20Sel-Sel%20Kanser%20Payudara%20Menggunakan%20Rangkaian%20Neural%20Peta-Peta%20Penubuhan-Diri%20%28Som%29_Nuryanti%20Mohd%20Salleh.pdf http://eprints.usm.my/58731/ |
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| Summary: | The rapid era of technology advancement has contributed a big impact against science
and technology approach. This development achievement in medical investigation could
helps to detect cancer infection which expanded mostly among the women. Cancer
detection in its earliest stage definitely is very important for an effective treatment. New
innovation in neural network methods which have become popular and its application in
medical field have enabled prediction of the cancer cells easier. Not just that, the ability
some of those methods have also been reported more accurate as compared to
conventional methods. Therefore, the purpose of this project is to investigate the
possibility of neural network methods in classifying breast cancer cells. In this project,
breast cancer cells classification was achieved based on Self-Organizing Maps Kohonen
2-Dimension neural network. Five inputs and an output were used to detect whether the
cells are cancerous or not. A set of training data was used to train the network. Back
Propagation algorithm has been chosen to train the network. A set of testing data was
used to determine the potential of this network model. The results showed that the
application of this neural network method was able to achieve 96.53% of classification
accuracy for training set and 92.75% for testing set. Discrete cell, X length and Y length
were the three dominant features that have been identified. This project was
implemented using ‘NeuralWorks Professional II / Plus’ software. |
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