Ali Kareem Obaid, Yaghoub Farjami
Alzheimer's disease AD, also known as dementia, is a disease resulting from an irreversible brain disorder that can develop gradually as the patient ages. Its distinguishing features include cognitive decline, memory loss, and psychological problems. Early diagnosis of this disease becomes difficult due to the complexity of the initial pathological changes. Other problems overlap with other types of diseases. Consequently, the computational complexity of analyzing neuroimaging data has become a new method, especially magnetic imaging MRI and PET positron tomography are possible thanks to recent advances in ML machine learning and DL deep learning. In this study, four basic learning methods —convolutional neural networks (CNNs), recurrent neural networks (RNNs), discrete polynomial networks (DPNs), and k-nearest neighbors (KNN)— were evaluated to detect and classify Alzheimer's disease. CNN networks have proven their high ability in binary and multi-category classification tasks, successfully distinguishing between people with the disease. Simultaneously, RNN architectures such as Long Short-Term Memory networks (LSTM) and Gated Recurrent Units (GRU) are particularly skilled at examining temporal trends in longitudinal datasets that monitor the development of diseases over a number of patient visits. Discontinued polynomial networks are beneficial for learning and improving the interpretability of linear representations. An investigation into methods for raising the polynomial order of extracted features will be carried out in order to learn more about this field. Even simple models like KNN might be a good place to start when used on tiny datasets that are obtained via volumetric measurements or clinical evaluations. These models are put through a thorough evaluation process using a number of benchmark databases, including ADNI, OASIS, MIRIAD, and AIBL. Their diagnostic performance metrics are evaluated using accuracy rates, sensitivity levels, specificity standards, and Area Under Curve-Receiver Operating Characteristic (AUC-ROC) scores. Our findings indicate that frameworks based on CNNs and RNNs demonstrate comparable performance to Naive Bayes classifiers; furthermore, ensembles of these neural network architectures show impressive generalization capabilities. In this article we emphasize the growing significance of integrating deep learning techniques across multiple modalities aimed at facilitating early diagnosis ，ongoing monitoring ，and personalized treatment strategies for Alzheimer’s Disease