Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
A pre-condition for identifying infectious disease and understanding the ecology of a species is by gender classification of the trabecular bone of an animal. Therefore, accurate gender classification on skeletal remains of nonhuman is essential for the research of nonhuman population. The tradition...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/53697/25/NurAfiqahSahadunMFC2014.pdf |
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| Summary: | A pre-condition for identifying infectious disease and understanding the ecology of a species is by gender classification of the trabecular bone of an animal. Therefore, accurate gender classification on skeletal remains of nonhuman is essential for the research of nonhuman population. The traditional method of classifying gender by comparative skeletal anatomy by atlas has raised issues with regard to accurate classification and challenge in management of data to identify optimum features and interpretation optimum features in a simple way. In this research all these three issues were addressed by using a process model developed specifically for gender classification. This research used two computational intelligence models, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results of simulations of both models were compared and ANN performed better than SVM. To improve the accuracy of ANN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected ANN classification model. The model is called PSO-ANN and has been developed by MATLAB and WEKA tools platform. Samples were taken from Ryan and Shaw collection. This sample contains proximal femur and proximal humerus. Comparisons of the performance measurement namely the percentage of the classification accuracy, sensitivity and specificity of the model were performed. The results showed that the ability of PSO-ANN in classifying gender outperforming the SVM and ANN model by acquiring 100% accuracy, sensitivity and specificity. Apart from that, the optimum features of the gender classification are extracted and translated into more understandable explanations using Decision Tree and compare the differences and similarities with the original features. These findings have shown that the proposed PSO-ANN is capable of successfully solving three issues in the existing method in gender classification. |
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