## 1 Introduction  
Alzheimer's disease (AD) is a neurological disorder characterized by the degeneration of brain cells, leading to memory loss and impairment in performing daily tasks. Despite extensive research, the exact cause of AD remains elusive [1], making it challenging to develop a definitive cure. However, effective management strategies can significantly enhance the quality of life for individuals affected by AD. Symptoms usually progress slowly until they reach a point where daily activities and self-care become impossible. Many individuals require ongoing support for issues related to motor skills and language skills. The prevalence of AD is substantial, with approximately 5.3 million individuals being diagnosed with AD in 2015, and projections suggest this number could escalate to 16 million by 2050 [2]. Various image processing and machine learning techniques have been proposed for diagnosing and predicting the progression of AD. However, MRI and PET (positron emission tomography) are widely used biomedical imaging modalities that offer valuable insights into the pathology of AD. Biomedical information assumes various formats, which include images and videos, each encapsulating different features of medical records [3, 4]. These data provide valuable insights into neuropathology, helping researchers and clinicians to gain a deeper understanding of various brain diseases. Currently, image processing and AI-enabled diagnosis of Alzheimer's disease represents a leading edge of medical research and technology. The combination of artificial intelligence and healthcare has facilitated the adoption of new methods for the early detection of AD [5]. The Alzheimer's Disease Neuroimaging Initiative (ADNI) database serves as a valuable resource for researchers, offering MRI scans of patients at various stages of AD progression. The data structure has four categories: Mild, Moderate, Very Mild, and Normal. Mild AD represents the transitional phase between age-related memory decline and significant dementia-related impairment, while Moderate AD is characterized by moderate cognitive and functional deficits. Patients in the Very Mild category experience rapid-onset dementia and exhibit challenges with language and judgment. Leveraging deep learning models enables the swift and accurate identification of disease-related features, facilitating early diagnosis and intervention [6]. Our study emphasizes the vital role of segmentation, feature extraction, and classification in neuroimaging research, particularly for diagnosing Alzheimer's disease. The proposed Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm integrates multiple layers to analyze MRI data, offering a novel approach compared to existing models that often utilize single-layer architectures or focus solely on MRI data. Additionally, CNN-LSTM demonstrates proficiency in handling diverse datasets and employs a multitasking learning approach, allowing the network to simultaneously analyze the progression and diagnosis of AD. Our research focuses on the following contributions:  
1. The evaluation results indicate that all existing models achieved less than 90%. Notably, CNNs demonstrated superior performance due to their simple yet effective architecture, enabling rapid training and testing. 2. This study aimed to develop a lightweight hybrid model for enhanced performance. By combining CNN and LSTM models in parallel, we introduce a novel hybrid Deep Neural Network (DNN) scheme. 3. Utilizing convolutional kernels of different sizes enhances the network's ability to capture essential features, Moreover, combining multiple features increases the representativeness. Therefore our hybrid model incorporates two smaller kernels (\(3\times 3\) and \(5\times 5\)) to replace the traditional large convolutional filter. 4. The integration of the enhanced Fuzzy C-means method with two different steps of watershed and feature extraction enhances model performance, achieving an average accuracy of 98.13%.