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This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers...
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
This research paper proposes an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical image like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. The manual analysis of tumor based on visual inspection by radiologist/physician is the conventional method, which may lead to wrong classification when a large number...
The past few years have seen a lot of applications of Hybrid Soft Computing approaches that seem to have completely replaced the traditional uni-system approaches. The added abilities that come from the hybrid approaches motivate their use in every system. Bio-Medical Engineering is yet another field which has seen a major change in the past few years. We find various new approaches being applied...
Thalassemia is a genetic defect that is commonly found in many parts of the world. Number of humans that are suffering from this disease is determined by screening the heterozygous population. This article investigates the thalassemia screening problem using the unconstrained functional networks classifier. The learning algorithm for this new scheme is briefly illustrated. The new intelligent system...
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