## 3 Results
### Experimental Results  
In our Python code development, we utilized the Spyder IDE, operating within Python version 3.8.18 and IPython version 8.12.2. Our computational resources were anchored on an Intel Core i5 7200 CPU, featuring four logical processors from the seventh generation family, and equipped with a memory capacity of 4096 MB. For tasks involving classification and rapid assessments of the results, we leveraged Kaggle's GPU P100, offering an extensive memory capacity of up to 73.1 GB and a substantial processor power. This GPU acceleration facilitated efficient training and evaluation of the deep learning model, ensuring high performance and enhancing scalability for computational tasks. In this section, we delve into a detailed presentation and discussion of our most significant experimental results from the segmentation and classification tasks. Additionally, we provide comparisons with both traditional methods and state-of-the-art models to showcase the effectiveness and advancements of our approach. 1. Comparison of Results of Traditional FCM and ImFcm  
Convergence was achieved at the 35 th iteration using the ImFcm algorithm, surpassing the maximum iteration threshold. As the iteration proceeded, the cost ratio decreased significantly, from 385.01 to 0.049. This substantial reduction indicates that the algorithm approached an optimal solution, demonstrating the efficiency of our technology in delivering results rapidly. In contrast, the standard FCM algorithm reached convergence at the 70 th iteration, starting from a higher cost value of 4907.9 and necessitating more time compared to the proposed method. Figure 7 illustrates the cost values for five different MRI scans, analyzed using both FCM and ImFcm techniques. This comparison highlights the improved performance and efficiency of the ImFcm algorithm. 2. CNN-LSTM Results with Traditional FCM  
Traditional FCM clustering was used for segmentation, followed by a CNN for AD classification. Figure 8 displays the training and validation results from the segmented brain MRI dataset. The classification task achieved a test accuracy of 50% over 50 epochs. One of the curves in the figure shows a red line for validation loss and a blue line for training loss, while another curve depicts a red line for training accuracy and a blue line for validation accuracy. According to this technique, the figure suggests that both the training and validation accuracies stabilized after the first initial 5 epochs, with the validation accuracy reaching 0.48 and remaining constant from epoch 5 to epoch 50. By epoch 50, the loss had decreased, with the validation loss converging to approximately 1.04 for both training and validation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Explanation** & **Math. Exp.** \\ \hline Accuracy (Acy) & Calculated by dividing the number of correct predictions by the total number of predictions made. & \(A_{cy}=\frac{T_{p}+T_{N}}{T_{p}+T_{p}+T_{N}+T_{N}}\) \\ \hline Sensitivity (Sny) & The sensitivity metric indicates the model’s effectiveness in detecting AD patients. & \(S_{ny}=\frac{T_{p}}{T_{p}+T_{N}}\) \\ \hline Specificity (Spy) & & \(S_{py}=\frac{T_{N}}{T_{N}+T_{p}}\) \\ \hline Precision (Prn) & Precision, on the other hand, evaluates the reliability of the diagnosis, or the proportion of individuals & \(P_{rn}=\frac{T_{p}}{T_{r}+T_{p}}\) \\ \hline F1 & The F1 score of the simulation is described as the harmonic mean of sensitivity and precision. & \(\text{F1}=2\times\left(\frac{S_{ny}\times P_{rn}}{S_{ny}+P_{rn}}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metric details. Figure 8: The accuracy and loss curves of classification for traditional FCM. Figure 7: Cost and time for FCM and ImFCm. 3. CNN-LSTM Results with ImFCm-WS  
Figure 1 illustrates the structure of the proposed ImFCm-WS-CNN-LSTM methodology, which outlines a systematic processing framework.