چكيده به لاتين
Functional Magnetic Resonance Imaging (fMRI) is a special type of magnetic resonance imaging that measures brain activity through changes in blood flow. More precisely, brain activity is measured by a low-frequency BOLD signal. The main goal of this dissertation is to provide a method of diagnosing Alzheimer's disease using these images. This method consists of four steps: reducing the size of the data set, initial weight of neural network weights, and classification using neural network. One of the main advantages of this method is the use of genetic evolution algorithm in the process of initial weighting of neural network weights, which increases the chances of achieving faster neural network convergence. Also, due to the use of dimension reduction step and feature selection, the computational complexity of the neural network is greatly reduced and the possibility of neural network preprocessing is reduced. Accordingly, this thesis proposes a system for identifying Alzheimer's lesions in fMRI images. First, after initial weighting of the neural network using genetic algorithm, in order to increase the processing speed and accuracy; in the preprocessing stage, it performed noise removal and increased the image quality. Is. In the next steps, by extracting the image features using ANN method, the isolated lesions for classifying suspected Alzheimer's lesions were classified into three classes: healthy, high-grade Alzheimer's lesions (malignant) and low-grade Alzheimer's lesions (controllable benign). The proposed algorithm for identifying and segmenting Alzheimer's lesions is applied to fMRI images from the MICCAI database. The result of the final classification algorithm shows that there is a significant improvement in the results of the proposed method.
