Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guest Editorial Special Issue on Particle Swarm Optimization
IEEE Transactions on Evolutionary Computation
Fetal lung maturity analysis using ultrasound image features
IEEE Transactions on Information Technology in Biomedicine
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
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This paper presents two novel feature selection techniques for the purpose of prostate tissue characterization based on Trans-rectal Ultrasound (TRUS) images. First, suspected cancerous regions of interest (ROIs) are identified from the segmented TRUS images using Gabor filters. Next, second and higher order statistical texture features are constructed for these ROIs. Furthermore, a representative feature subset with the best discriminatory power among the constructed features is selected using two artificial life techniques: the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO). Both the PSO and ACO are tailored to fit the binary nature of the feature selection problem. The results are compared to the results obtained using the Genetic Algorithm (GA) feature selection approach. When Support Vector Machine (SVM) classifier is applied for the purpose of tissue characterization, the features obtained using the PSO and ACO outperforms the features obtained using the GA, i.e., they are capable of discriminating between suspicious cancerous and non-cancerous in a better accuracy. The obtained results demonstrate excellent tissue characterization with 83.3% sensitivity, 100% specificity and 94% overall accuracy.