There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method.
A hslract-Indexing and retrieral in biomedical image databases is a challenging problem. Constructing large-scale indexing solutions is typically limited by a choice of appropriate features, complexity constraints of the engine and a way how to combine retrieval results to have a stronger one. Combination of standard feature extraction routines with specific knoalcdge on a subject, such as precise automatic object segmentation and medical parameters estimation is the first key factor to achieve high accuracy and robustness of tho indexinglretrieval solution. W e arc developing a search cngine based on a TTAlO algorithm, which stores data in hierarchical fashion, with logarithmic complexity to access a large data repository in real-time. We propose to use AdaBoost technique to combine independent search results into more robust and accurate one. Initial results on a database of more than 80.000 ultrasound images demonstrate good accuracy and fast speed.
Abstract-Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are d ifferent approaches and algorith ms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage imp lies a choice of a variety of machine learning methods (Support Vector Machines, Artificial Neural Networks, etc), the first and second stages play a critical ro le in performance and accuracy of the classification system. In this paper we present initial study of feature ext raction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA -based method for feature reduction stage is proposed and compared with conventional methods such as Principal Co mponent Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in co mparison with the current methods.
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