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In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a...
This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the...
Breast cancer is the most common cancer among women. To assist the ultrasound (US) diagnosis of solid breast tumors, the lobulated contour feature quantified by boundary-based corner counts is studied to classify breast tumors as malignant or benign. The corner points in this research was detected based on wavelet transform (WT), and the classification selected through comparison is support vector...
A previously proposed approach based on RBF neural networks for detecting anomaly location is extended to estimate the anomaly size. First, a predefined number of threshold values are selected in the range of possible anomaly sizes. Next, RBF neural networks are used as classifiers to classify the anomaly size as being smaller or larger than each threshold value. The inputs of the classifiers are...
In this paper, computer aided diagnosis (CAD) is applied to the brain CT image processing. In addition to the 3 classical types of features, i.e. gray scale, shape and texture, the symmetric feature based on the characteristics of human-brain CT image is extracted. Inductive learning techniques, See5 and RBFNN (radial basis function of nerve network) are used to build classifiers for normal and abnormal...
Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the naive Bayes (NB) fusion rule is utilized to combine a group of radial basis function (RBF)...
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