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 ...

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...

computer aided pulmonary nodule detection has been among major research topics lately to help for early treatment of lung cancer which is the most lethal kind of cancer worldwide.some evidence suggests that periodic screening tests with the ct of patients will help in reducing the mortality rate caused by the lung cancer. acomplete and accurate computer aided diagnosis (cad) system for detectio...

land cover map is important for many urban planning and management activities. in this study, in order to produce land cover map of arak city, digital image of liss-iii scanner acquired on 16 june 2006 were employed. first of all, geometric correction with rmse 0.58 pixel was applied. considering the mountainous condition of the study area, topographic correction was applied to the image. in su...

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

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 ...

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...