## 2 Materials and Methods  
### Dataset Description  
The benchmark dataset research on the \"Kaggle\" website is available online: [[https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images](https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images)]([https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images](https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images)) (accessed on 11 February 2023). It provided MRI pictures of Alzheimer's disease for this study [38]. Kaggle serves as a platform for providing online datasets for research and analysis in various fields. To expedite the development of enhanced algorithms for diagnosing and treating Alzheimer's disease, we chose this dataset owing to its complete freedom, availability in diverse categories, and relatively small hard disk size, setting it apart from other popular datasets in the field. This manually collected dataset comprises MRI images verified and classified by [PERSON] [38]. Serving as valuable resources for training and testing deep-learning models with the objective of accurately predicting the stage of Alzheimer's disease. By affording researchers and practitioners an opportunity to create algorithms for precise Alzheimer's disease diagnosis, this dataset assumes a crucial role. Additionally, it contributes to the development of effective treatments. As the global burden of Alzheimer's disease escalates, this dataset gains significance in advancing our understanding of the disease and improving patient outcomes [36].  
A total of 6400 photos make up the Kaggle Alzheimer's classification dataset (KACD). The dataset was divided into four groups: 896 mild AD, 64 moderate AD, 3200 normal, and 2240 very mild AD. To test the models, 20% of the dataset was used for testing, while 80% was used for training and analysis. The sample included 2560 normal controls, 717 participants with mild AD, 52 with moderate AD, and 1792 with very mild AD. A typical sample from each dataset class is shown in Figure 1. Doctors use the Hippocampal area as a biomarker to diagnose Alzheimer's disease (AD) with great accuracy, making it a significant factor. However, hippocampus volume alone cannot predict early stages. According to prior study [4], cortical regions and thickness affect the illness's progression. Due to its high resolution and contrast for soft tissues, structural MRI is used to evaluate the parietal, temporal, hippocampal, entorhinal cortex, and ventricular atrophy [6]. Different brain regions are modified depending on illness progression [4].  
This study's objective, as depicted in Figure 1, is to demonstrate how cognitive decline manifests differently in areas that have not experienced any disease and areas that have experienced the worst cases of the same disease [10]. In contrast to the moderate stage, individuals in the very mild and light stages of dementia exhibit a somewhat better level of independence in their functioning. However, due to notable memory impairment, they often require some level of support with various everyday activities. The severity stage is characterized by a prolonged duration compared to the very mild and mild stages. Duringthis stage, the patient has a progressive deterioration of their physical condition, ultimately leading to mortality [39].