Self-organizing maps
Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
GI '05 Proceedings of Graphics Interface 2005
Automatically Finding Images for Clinical Decision Support
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Robot Navigation Using Camera by Identifying Arrow Signs
GPC-WORKSHOPS '08 Proceedings of the 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
ImageCLEF: Experimental Evaluation in Visual Information Retrieval
ImageCLEF: Experimental Evaluation in Visual Information Retrieval
Mastering MATLAB
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks EANNs and Evolving Artificial Neural Network Ensembles EANNEs computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network ANN training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization LVQ winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed'08 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach.