Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

Transfer learning has become extremely popular in recent years for tackling issues from various sectors, including the analysis of medical images. Medical image analysis has transformed medical care in recent years, enabling physicians to identify diseases early and accelerate patient recovery. Alzheimer’s disease (AD) diagnosis has been greatly aided by imaging. AD is a degenerative neurological condition that slowly deprives patients of their memory and cognitive abilities. Computed tomography (CT) and brain magnetic resonance imaging (MRI) scans are used to detect dementia in AD patients. This research primarily aims to classify AD patients into multiple classes using ResNet50, VGG16, and DenseNet121 as transfer learning along with convolutional neural networks on a large dataset as compared to existing approaches as it improves classification accuracy. The methods employed utilize CT and brain MRI scans for AD patient classification, considering various stages of AD. The study demonstrates promising results in predicting AD phases with MRI, yet challenges persist, including processing large datasets and cognitive workload involved in interpreting scans. Addressing image quality variations is crucial, necessitating advancements in imaging technology and analysis techniques. The different stages of AD are early mental retardation, mild mental impairment, late mild cognitive impairment, and final AD stage. The novel approach gives results with an accuracy of 96.6% and significantly improved outcomes compared to existing models.

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this study, we utilized a combination of biomarkers, including magnetic resonance imaging (MRI), electronic health records, and cerebrospinal fluid (CSF), to classify subjects into three groups based on clinical tests—normal cognitive controls (CN), mild cognitive impairment (MCI), and AD. To determine the significant parameters, we employ a novel technique that utilizes sparse autoencoders to extract features from CSF, clinical data, and convolutional neural networks’ (CNN’s) MRI imaging data. Our results indicate that deep learning methods outperform traditional machine learning models such as decision trees, support vector machines, random forests and K-nearest neighbors. The proposed method significantly outperforms traditional models, achieving an accuracy of 0.87 for CN versus AD, a precision of 0.93 for CN, and a recall of 0.88 for AD on the external test set. The integration of various data modalities and the application of deep learning techniques enhance the prediction accuracy, demonstrating the potential for improved diagnostic tools in clinical settings.

Alzheimer’s Disease (AD) is a neurological disorder that affects cognitive functions, including memory, thinking, and behavior. Early detection of Alzheimer’s disease is critical for effective treatment and management of the condition. Deep Learning (DL) is a powerful tool that can be used for AD detection and diagnosis. DL algorithms can learn patterns and features in large datasets that can be used to classify and predict the presence of Alzheimer’s Disease. The most common approach is to use brain imaging techniques, such as computed tomography and brain MRI scans, to extract features that are characteristic of Alzheimer’s Disease. Transfer learning-based deep learning models can be effective in detecting Alzheimer’s disease from medical images. Transfer learning involves using pre-trained neural network models as a starting point and fine-tuning them to suit a specific task, such as Alzheimer’s disease detection. This paper focuses on classifying AD patients into various stages (early mental retardation, mild mental impairment, late mild mental impairment, and final Alzheimer’s stage) by utilizing transfer learning with ResNet50, VGG16, and DenseNet121 along with CNN networks on a large dataset. The work classifies Alzheimer’s patients into various stages using transfer learning with ResNet50, VGG16, and DenseNet121 along with CNN on a large dataset. The model is trained and tested on ADNI data using Keras API and divides the MRI images into: EMCI, MCI, LMCI, and AD. The performance of VGG16, DenseNet121, and ResNet50 outperformed other models significantly. The results demonstrate a significant improvement in accuracy compared to previous approaches, with a final accuracy of 96.6%.