## 1 Introduction
### Literature Review
In [14], a Multiplan CNN technique was proposed and applied to 1500 MRI datasets sourced from the ADNI dataset for the classification of AD, MCI, and NC. The method incorporates the brain extract tool (BET2) to eliminate non-brain areas from the MRI scans. The suggested architecture relies on a sequential CNN approach to discern spatial structural data. Through experimentation, an overall classification accuracy of 93% was attained across the three classes. In [15], a 2 DCNN method is introduced for the classification of AD and MCI utilizing 3312 MRI scans. BET2 is employed for skull stripping during the image preprocessing. The proposed model is built upon LENet-5, with modifications to the activation function (Leaky ReLU) and output function (sigmoid). Moreover, batch normalization is incorporated to enhance the stability of the learning process. This fine-tuned model achieved the highest accuracy of 84% in successfully classifying AD. In [16], the authors introduced a fine-tuned ResNet18 model designed to classify MCI, AD, and CN from MRI and PET data. Their fine-tuned model incorporated transfer learning and a weighted loss function to ensure balanced class weights. Furthermore, the mish activation function was utilized to enhance the classification accuracy. The model achieved a classification accuracy of 88.3%. In the study presented in [17], two hybrid algorithms were employed to segment MR brain images of Alzheimer's disease patients. These algorithms combined nature-inspired techniques, employing a fusion of particle swarm optimization and a genetic algorithm (PSO_GA), and a combination of the whale optimization algorithm and a genetic algorithm (WOA_GA). The objective was to enhance the performance of SVM and AdaSVM classifiers. The experimental results indicated that PSO_GA achieved the highest accuracy, albeit requiring more computational time compared to the WOA and WOA_GA methods. Notably, WOA_GA exhibited superior accuracy compared to the majority of the algorithms tested. In the research in [18], voxel-based morphometry (VBM) was utilized for the early detection of [PERSON]'s disease using quantitative susceptibility mapping (QSM) and vector-based modeling. The voxel extraction algorithm was refined to improve its detection performance, and the revised index was compared with the traditional VBM-based index on specific voxels in QSM and VBM images. The index was calculated using a linear support vector device. The proposed improved index demonstrated an AUC of 0.94 between AD and NC, indicating its effectiveness in early detection. In another study [19], MRI datasets from the Open-Access Series of Image Studies (OASIS) were employed, and a blend of K-means and watershed algorithms was applied to segment the hippocampus, a region of the brain affected by [PERSON]'s disease. The results illustrated that the integrated segmentation accurately identified and diagnosed this disease, showcasing the potential of such techniques in medical imaging analysis. Our combined use of the Improved Fuzzy C-means, watershed, and CNN-LSTM techniques under the developed framework brings complementary advantages. Specifically, CNN-LSTM, with an improved structure containing few layers, allows the identification of [PERSON]'s disease abnormalities with the highest accuracy, while ImFCm-WS extracts useful features from MRI data. All of these efforts were integrated together to improve the accuracy and reliability of Alzheimer's disease classification to support early identification and personalized medicinal approaches. The aim of the current research was to test the efficacy of the proposed method in AD classification and investigate its convenience in AD diagnosis. The reported results were then compared to the currently leading classification approaches according to precision, sensitivity, and specificity, and ours reached values of up to 98%. Collaboratively, we designed the current investigation to guide the development of AD diagnosis and refine the diagnosis of this disease through computational techniques.