Over the past few years, medical image processing has increased in use of deep learning algorithms, especially in the analysis of magnetic resonance (MR) scans. MRI is a crucial diagnostic tool for Alzheimer's disease (AD), a prevalent type of dementia that ranks seventh among fatal illnesses globally. As there is no known cure for Alzheimer's disease, early detection and intervention are vital to prevent its irreversible progression. This study proposes a comprehensive framework for detecting Alzheimer's disease that employs convolutional neural networks (CNNs) and deep learning approaches. We applied transfer learning to pretrained deep learning models rather than training them from scratch. Three distinct pretrained CNN models (VGG-19, ResNet-50, and Inception V3) with a fine-tuned transfer learning approach were used for five-way classification of AD. We employed the ADNI dataset, which includes MRI scans from 608 patients across five classes: Alzheimer's disease (AD), early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and normal control (NC). The models' performance was evaluated based on eight metrics: accuracy, precision, sensitivity/recall, specificity, error rate, false positive rate, F1-score, and kappa. Our findings indicate that the ResNet-50 architecture outperformed other pretrained models, achieving the highest overall accuracy of 98.7% for multiclass AD classification. Additionally, the ResNet-50 model excelled in classifying the EMCI category with an accuracy of 99.25%, indicating its effectiveness in detecting early signs of memory impairment. The proposed framework surpasses the performance of previous studies in terms of overall accuracy, sensitivity, and specificity, setting a new benchmark for five-way AD classification. The outcomes of this study will contribute significantly to early prevention efforts by enabling Alzheimer's disease to be detected before it progresses irreversibly. In conclusion, this research represents a promising approach for improving the early detection and classification of Alzheimer's disease using deep learning methods with MRI data.

Alzheimer's disease (AD) is a brain disorder that impairs one's ability to function on a daily basis. Machine learning (ML) has significantly helped clinicians in quickly and accurately identifying AD through imaging-based methods. Advanced neuroimaging techniques have made it possible to detect structural and functional abnormalities in the brain associated with AD. However, applying ML to clinical settings poses challenges due to differences in data distribution between training and test datasets, which can significantly impact model performance. To address this challenge, the domain adaptation (DA) approach is necessary, as data from different distribution domains present a performance gap. This review focuses on current research involving ML and DA techniques for early AD detection using neuroimaging data, and aims to encourage further research efforts to improve the clinical workflow for diagnosing and treating AD. The review also includes a discussion of the limitations of current studies and possible research directions.

Dementia is a medical syndrome resulting in substantial memory loss or deterioration and other cognitive capabilities, beyond the normal aging process [2]. Alzheimer's disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. With the growing lifespan, the number of AD patients is also increasing, and estimated that by the year 2050, 135 million people will be affected [1]. With age being the predominant dementia factor, the dominance ranges from 1-2 percent in the age group of 65 to 30 percent at 85. AD is a progressive, irreversible and neurodegenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. Individual diagnoses of AD are now based mostly on neuropsychological testing and clinical examination, but only post-mortem brain study may confirm the final diagnosis. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer's disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks (CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioural and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinicians.