Methods for combining experts' probability assessments
Neural Computation
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Analysis of Breast Cancer Using Data Mining and Statistical Techniques
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Signal Processing Systems
Some Remarks on Chosen Methods of Classifier Fusion Based on Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
International Journal of Applied Mathematics and Computer Science - Applied Image Processing
Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
Introduction to Machine Learning
Introduction to Machine Learning
Breast-Cancer identification using HMM-fuzzy approach
Computers in Biology and Medicine
Bayesian analysis of linear combiners
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
Texture analysis in perfusion images of prostate cancer-A case study
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Prostate cancer grading: Gland segmentation and structural features
Pattern Recognition Letters
“Good” and “bad” diversity in majority vote ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
Cost-Sensitive splitting and selection method for medical decision support system
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Combining classifiers under probabilistic models: experimental comparative analysis of methods
Expert Systems: The Journal of Knowledge Engineering
Adaptive splitting and selection algorithm for classification of breast cytology images
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Breast cancer is the most common type of cancer among women. As early detection is crucial for the patient's health, much attention has been paid to the development of tools for effective recognition of this disease. This article presents an application of image analysis and classification methods for fine needle biopsy. In our approach, each patient is described by nine microscopic images taken from the biopsy sample. The images are related to regions of the biopsy that seem interesting to the physician who selects them arbitrarily. We propose four different hybrid segmentation algorithms dedicated to processing these images and examine their effectiveness for the nuclei feature extraction task. Classification is carried out with the usage of a classifier ensemble based on the Random Subspaces approach. To boost its effectiveness, we use a linear combination of the support functions returned by the individual classifiers in the ensemble. In the proposed medical support system, the final decision about the patient is delivered after a fusion of nine separate outputs of the classifier - each for a different image. Experimental results carried out on a diverse dataset collected by the authors prove that the proposed solution outperforms state-of-the-art classifiers and shows itself to be a valuable tool for supporting day-to-day cytologist's routine.