Abstract
With the growth of application of computers in the generation and analysis of biomedical data, a variety of computerized methods and algorithms have been proposed to optimize the process of acquisition and analysis of the data. Although advanced computerized techniques have provided the means for more precise diagnosis, the interpretation of the recorded data in some cases is an issue due to the large amount of the data or complexity of it. While most of the existing work in the literature consider supervised techniques for analysis of the collected data, the use of unsupervised techniques in the area of analysis and classification of biomedical signals is relatively unexplored compared to supervised approaches. In general, the investigation of application of unsupervised techniques for analysis of biomedical signals can be worthwhile from different view points. In some cases, biomedical databases tend to contain a large amount of data. Genomic databases or pathological speech databases are examples of this kind. The development of any supervised method for analysis of such databases requires precise manual labeling of the data, which can be extremely costly. However, the use of an unsupervised classifier can be beneficial to accelerate the process and to acquire information about the structure of the dataset. In addition, the characteristics of the collected biomedical data can be affected by the recording process. In this work application of unsupervised learning in two biomedical signal processing problems is investigated. In the first problem, fuzzy C-means clustering has been used in design of a computer aided diagnosis method for detection of abnormalities in small bowel capsule endoscope images. The performance of the system shows an accuracy of 76which is an acceptable rate for an unsupervised method. In the second case, self organizing tree maps (SOTM) has been applied to audio signal classification for hearing aids. An accuracy of 96% was achieved for discrimination of human voice from the environmental noise, which is one the major classification scenarios for hearing aid applications.
Highlights
In the first Chapter, background infonnation on computer-aided diagnosis (CAD) in niCdical inw,ging and audio classification for hearing aid application is provided
Sometimes labeling a large dataset can be surprisingly costly a.nd not feasible" lJnsupcrviscd classification can be used to discover the natural groupings that exist in the dataset and use supervision only to label the clusters found
Unsupervised classification is known as data clustering which is a generic label for a variety of procedures designed to natural groupings, or dusters, in rnultidirnensional data, based on rnea.sured or perceived sirnilarities ~unong the patterns [42]
Summary
In the first Chapter, background infonnation on CAD in niCdical inw,ging and audio classification for hearing aid application is provided. Unsupervised Learning in :1\tiedical In1age Classification: In this chapter the application of fuzzy C-n1eans clustering n1ethod for detection of abnormalities in the small intestine irnages will be described. NSUPER~I~ED classific-ation is a struct decision boundancs based pattern recognition technique on unlabeled dataset. Unsupervised classification is known as data clustering which is a generic label for a variety of procedures designed to natural groupings, or dusters, in rnultidirnensional data, based on rnea.sured or perceived sirnilarities ~unong the patterns [42]. The problen of unsupervised classification or clustering is very challenging because data can contain e First, in smne cases labeling a large dataset can be surprisingly costly. One example could be the application of land-use classification in renwte sensing In this case obtaining the "ground truth" infonnation for the samples, \Vhieh is the category for each pixel in an in1age, requires one to visit the specific site assoeiated with the pixeL Another exmnple is speech classification. If a classifier can be crudely designed on a small labeled dataset and run without supervision on a large unlabeled dataset, n1uch tirne and trouble can be saved
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