image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

breast cancer is the second largest cause of cancer deaths among women. at the same time, it is also among the most curable cancer types if it can be diagnosed early. this paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. the proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. in t...

sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. in this paper, a combination of three kinds of classifiers are proposed which classify the eeg signal into five sleep stages including awake, n-rem (non-rapid eye movement) stage 1, n-rem stage 2, n-rem stage 3 and 4 (also called slow wave sleep), and rem. twenty-five all night recordings...

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...