Averaging regularized estimators
Neural Computation
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Machine Learning
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Prediction of Speech
Feature selection toolbox software package
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Feature selection with neural networks
Pattern Recognition Letters
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Intelligent vocal cord image analysis for categorizing laryngeal diseases
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Multiple feature sets based categorization of laryngeal images
Computer Methods and Programs in Biomedicine
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Neural Computing and Applications
Predictor output sensitivity and feature similarity-based feature selection
Fuzzy Sets and Systems
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
Using the patient's questionnaire data to screen laryngeal disorders
Computers in Biology and Medicine
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
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Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data. Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search. Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging. Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.