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Health information technology is spreading all over the world to provide right medical advice for healthcare. In this context, this paper describes the application of data mining techniques to identify jaundice by analyzing liver function test reports. Artificial neural network and support vector machine classifiers are employed here for classification on the basis of liver condition. Since correct...
Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework...
This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation...
We propose a new ultrasonic image analysis system that can be utilized as an effective tool in classifying liver states as normal, hepatitis, or liver cirrhosis. In this system, we first define suitable settings for the ultrasonic device, then remove the inhomogeneous structures from the area of interest in the image, and then, by using the forward sequential search method, look for the useful texture...
Liver diseases are among the leading causes of death worldwide. The most useful approach for controlling the growth of diseases to reach at severe condition is to treat these diseases at the early stages. Early treatment requires early diagnosis, which needs an accurate and reliable diagnostic procedure. The aim of this study is to develop a computer-aided diagnostic system to achieve aforementioned...
The machine learning techniques, SVM, decision tree, and decision rule, are used to predict the susceptibility to the liver disease, chronic hepatitis from single nucleotide polymorphism(SNP) data. Also, they are used to identify a set of SNPs relevant to the disease. In addition, we apply backtracking technique to couple of feature selection algorithms, forward selection and backward elimination,...
In this paper, a kernel-based classifier for automatic liver diseases diagnosis of CT images is introduced. Three kinds of liver diseases are identified including cyst, hepatoma and cavernous hemangioma. The diagnosis scheme includes two steps: features extraction and classification. The features, derived from gray levels, co-occurrence matrix, and shape descriptors, are obtained from the region of...
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