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Feature extraction is an important technique for complex, multivariate data containing various attributes. In this paper, we propose new detection schemes to help diagnosing epilepsy and detecting the onset of epileptic seizures. These schemes are based on the dynamic principle component analysis (PCA) approach and on partially extracted features. We propose a detection performance measure for evaluation...
Early detection of melanoma is one of the greatest challenges of dermatologic practice today. A new diagnostic method, the “ELM 7 point checklist”, defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been presented as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. In this paper...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
The particles in urinary microscopic images are hard to classify because of noisy background and strong variability of objects in shape and texture. In order to overcome these difficulties, firstly, a new method of texture feature extraction using the distance mapping based on a set of local grayvalue invariants is introduced and the feature is robust to the shift and rotation. Secondly, we reduce...
The analyzing quantitative kinetics gait data is very important in medical diagnostics as well as in early identification of gait asymmetry. The paper investigated the application of kernel-based technique in kinetic gait data with nonlinear property for gait feature extraction and classification. Its basic idea was that Kernel principal component analysis (KPCA) algorithm was employed to extract...
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