Behrouz Bidabad

Early diagnosis of Alzheimer's disease (AD) is increasingly important due to its rising prevalence and significant impact on individuals, families, and healthcare systems. Hippocampal atrophy is a well-established and significant biomarker for AD. Advanced techniques like MRI imaging and surface parameterization have shown considerable promise in improving the accuracy and speed of AD diagnosis. This study aims to utilize the Ricci flow method to map the 3D hippocampal surface to a 2D sphere and extract relevant features for early AD detection. The process involves several key steps: inputting an MRI scan and preprocessing to isolate the hippocampal surface, applying the Ricci flow to map this surface to a sphere, constructing a feature vector using Linear Discriminant Analysis (LDA) and Kernel LDA, and employing various classifiers to diagnose AD, with model evaluation based on the ADNI dataset. Experimental results reveal that combining Ricci flow-based feature extraction with Kernel LDA significantly improves diagnostic accuracy. The model achieves classification accuracies of 97.28% (NC/AD), 96.14% (NC/EMCI), 96.45% (NC/MCI), 94.83% (EMCI/LMCI), 95.84% (MCI/AD), and 95.37% (LMCI/AD). Additionally, it attains 93.65% and 92.30% accuracy in three-way and four-way classification tasks, respectively. These results outperform most reviewed studies and are comparable to others. This research highlights the potential of merging advanced 3D imaging techniques with mathematical models to enhance diagnostic precision, emphasizing the critical role of early detection in the effective treatment and management of Alzheimer's disease.

Automated feature extraction from MRI brain scans and diagnosis of Alzheimer's disease are ongoing challenges. With advances in 3D imaging technology, 3D data acquisition is becoming more viable and efficient than its 2D counterpart. Rather than using feature-based vectors, in this paper, for the first time, we suggest a pipeline to extract novel covariance-based descriptors from the cortical surface using the Ricci energy optimization. The covariance descriptors are components of the nonlinear manifold of symmetric positive-definite matrices, thus we focus on using the Gaussian radial basis function to apply manifold-based classification to the 3D shape problem. Applying this novel signature to the analysis of abnormal cortical brain morphometry allows for diagnosing Alzheimer's disease. Experimental studies performed on about two hundred 3D MRI brain models, gathered from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our descriptors in achieving remarkable classification accuracy.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

This paper investigates the short-time existence and uniqueness of Ricci flow solutions on Finsler manifolds. The main results of this paper are theorems demonstrating the short-time existence of the flow solution for $n$-dimensional Finsler manifolds and the uniqueness of the solution for isotropic Finsler manifolds. Two examples are also presented to illustrate the results.