Sharif Digital Repository

in this paper we present an integrated classifier that is used in mammogram MR-image for classification of breast cancers and abnormalities using a Multi-stage classifier, the method developed here first classifies mammograms into normal and abnormal and then for abnormal cases determines that if the case cancer is benign or malignant and also determine the type of breast cancer. In this paper there are two main topics that must be considered. First one is selection of good features and second is designing a good structure for classifier. In this study, the features are a combination of some features that are extracted from Spatial Grey Level Dependency matrix and some statistical descriptor... 

In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The... 

In this study, we have proposed a novel method investigates MR-Images for normal and abnormal brains which effected by Alzheimer's Disease (AD) to extract 336 number of different features based on texture analysis. Before applying this algorithm, we have to use a registration method because of variety in size of normal and abnormal images. Consequently, the output of Texture Analysis System (TAS) is a vector containing 336 elements that are features extracted from texture. This vector is considered as the input of the Artificial Neural Network (ANN) which is feed-forward one. The features extracted from the Gray-level Co-occurrence Matrix (GLCM) have been interpreted and compared with normal...