Most studies now focus on early AD diagnosis to find treatments and enhance patients' quality of life by studying the pathogenic mechanisms of different phases of AD (Basaia et al., 2019), shown in Figure 3. Preclinical AD is the first stage, where blood, brain, and CSF abnormalities occur without symptoms. Thus, contemporary life's beginning is no longer clear. The typical test struggles to detect AD early on, when the changes are too minor. Early AD detection is hindered by stigma, a lack of dementia care training, and the importance of early diagnosis (Dubois et al., 2009). Confusing dementia-like disorders with cognitive impairment (delirium, melancholy, vitamin deficiencies). This stage may begin 20 years before symptoms appear. An earlier AD experience study found significantly stronger connections between early linguistic skills and AD later in life (Bhandare et al., 2016). Stage two is modest cognitive impairment. Memory loss or other mental disability in those who can do most of their daily chores alone is called MCI (Sarraf et al., 2016). The patient's family and close friends may detect cognitive ability issues at this time, but they don't influence their everyday lives (Dubois et al., 2009). Only 10%−15% of MCI patients develop AD (Baskar et al., 2019; Bi et al., 2020), and it's unclear why certain people get dementia. After an MCI diagnosis, a particular diagnostic process must begin to identify the disease or condition that causes the impairment (Borghesani et al., 2008). The former describes memory-impaired patients. However, the latter identifies patients with cognitive problems in areas other than memory, such as attention or language processing. MCI increases AD risk, according to theory (Brown, 2015). Other stages of AD include late moderate cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and cognitive normal (CN). AD's last stage, dementia, impairs daily life due to memory, cognitive, and behavioral issues. Table 1 lists AD phase symptoms. Early AD diagnosis helps patients take preventive measures, helps physicians identify AD progression risk, informs patients about the severity of the condition, and motivates them to use medications and lifestyle changes (Liu et al., 2014b). Therefore, the importance of an early AD diagnosis is as follows in the next subsection.

The issue is that AD symptoms are frequently misdiagnosed as an effect of aging, delaying medical consultation until it is too late and leading to a delayed diagnosis (Brown et al., 2020). In the 2008 study (Busche et al., 2008), 64% of carriers admitted that before diagnosis, they believed changes in patients' behaviors were signs of aging. Sixty-seven percent of respondents agree that the diagnosis has been delayed. Moreover, once in the hands of experts, a precise diagnosis of AD is still tricky. Even the most skilled practitioners cannot correctly diagnose AD in 10%−15% of cases (Çayir et al., 2018). The only way to accurately diagnose the illness is through a postmortem examination of the brain. A clinical diagnosis of AD may currently be made with an accuracy of ~90% using a patient's medical history, neurological and physical examinations, laboratory tests, brain imaging, and neuropsychological assessment (Chakraborty et al., 2017).

Early diagnosis allows patients to access programs and support services and medications that can postpone some symptoms and work better in the early stages (Hennig et al., 2003). To diagnose AD early, even small symptoms must be recognized. Given the multimodal character of AD symptomatology, the best early AD detection method cannot simply rely on measurements of a single domain (Chitradevi and Prabha, 2020), such as physiological or behavioral symptoms, but rather on the combination of many modalities, which may allow to identify all subtle alterations of all domains from the start and contrast them with other symptoms for an appropriate diagnosis (Choi et al., 2020). Several research groups use neuroimaging techniques like MRI, fMRI, and PET to classify AD and MCI.

Numerous researchers are studying early AD diagnosis using machine learning (ML) and DL methods to discover a treatment. Even though ML models are more effective than ever in many domains, they still need human specialists to solve some problems. A flexible DL model algorithm learns from automatic feature extraction approaches regardless of prediction accuracy (Mahrishi et al., 2020). The DL model learns and predicts features. ML cannot automatically extract features. The following section discusses ML and DL AD diagnoses.

Over the past 20 years, AI and ML have gained popularity and relevance. ML is semi-automated data knowledge retrieval (Bardis et al., 2020). ML feeds an algorithm that understands input-output relationships. After training, the computer can estimate a new data point's value or class (Islam and Manivannan, 2017). Figure 4 shows ML vs. DL, a framework that can be learned without design. AI can run machines and act like people. The ANN is utilized in DL learning (Ayodele, 2010). ANN architecture is layered. DL types include RNNs, auto-encoders, and CNNs (Hosseini et al., 2020). DL frameworks let programmers build and test DL apps. PyTorch, TensorFlow, Keras, Caffe, Theano, and more open-source DL frameworks are available online. Many notable open-source frameworks are ranked by developer's group, supported languages, and useful programs. It compares ML with DL based on data, training time, and interpretability. The fundamental difference between ML and DL is in Table 2. A suitable function is manually extracted from the data by ML. The extracted function is used to update model parameters for accurate prediction (Wei et al., 2019). This is irrelevant since DL automatically retrieves data functions. DL also uses end-to-end learning, where the network delivers data and tasks (Chen et al., 2018).

High-performing DL models require 1,000 records (Guo et al., 2019). Recent years have seen DL's speech recognition (Deng and Platt, 2014; Noda et al., 2015), image recognition (Pak and Kim, 2017; Wu and Chen, 2015), and speech analysis applications assist healthcare (Li et al., 2017), pharmacy (Wang F. et al., 2019), natural language processing (Otter et al., 2020), and other sectors (Young et al., 2018). Recently, many DL recommendations (Alom et al., 2019) have emerged, which may be categorized into algorithms, which will be explored in Section 3. To retrieve the original image and meet feature extraction, selection, and classification criteria, several pre-processing steps are needed. Most machine learning studies involve preprocessing before data manipulations, whereas deep learning surveys may not. Most research preprocesses raw data with intensity normalization, recording, skull dissection, and motion correction (Zheng et al., 2017).

The structure of this survey is as follows: Section 2 discusses the materials and method of this review, and Section 3 discusses the detailed investigation of AD detection. Section 4 mentions the various datasets for diagnosing AD. Performance measures for detecting AD are discussed in Section 5. In Section 6, the different challenges of the AD diagnosis are presented, and Section 7 concludes with the substantive work of the survey.

The first and foremost step in the diagnostic pipeline is data acquisition, which involves collecting necessary datasets, including imaging data or other biomarkers for diagnosis. The quality and diversity of the data have a direct impact on the model's overall performance. Errors created here can spread across the system and disturb clinical operations. Generally, the following factors can help a digital imaging department integrate image acquisition equipment (Andriole, 2006).

When it comes to acquiring images from intrinsically digital modalities like computed tomography (CT) and MRI, the industry standard for digital imaging and communication in medicine is called DICOM capture (Paul et al., 2024). The DICOM method ensures the acquisition and transfer of images from the modality at its full bit depth and spatial resolution. However, the DICOM method only allows for 8 bits of image quality or 256 gray levels, but the direct recording of digital data from essentially digital modalities is the preferred acquisition method, providing the highest quality images for clinical needs.

Images contain information that can be gained through a variety of modalities or procedures and describe the internal characteristics of the body. This process can produce neurological data and combine multiple physical principles (Theodore et al., 1986). The task determines the choice of these modalities, the model being employed, and the researcher's preference. Numerous organizations, such as clinics, hospitals, X-ray facilities, and online platforms, provide datasets (Wu et al., 2019).

An early diagnosis of AD is more likely if representative causes, such as blood, urine, saliva, and nasal mucosa, are thoroughly analyzed (Sakatani et al., 2020). However, this method cannot be utilized as a routine detection tool because of the difficulties in gathering samples. Furthermore, the subject's body will inevitably move slightly because of the extended acquisition period. Building DL models cannot directly use real-world data since it contains noise and missing values in an unprocessed state.

Pre-processing procedures for data are required to transform such noisy data into a format that a machine can read. The original image must undergo several preprocessing steps to meet feature extraction, classification, and selection requirements. Most studies require preprocessing before data operations, particularly in ML. Some investigations might need something other than preprocessing with the aid of DL. However, most research continues preprocessing raw data using motion correction, registration, skull dissection, and intensity normalization (Tong et al., 2014).

This section focuses solely on the preprocessing of MRI for AD identification. Nonetheless, if essential for an improved result, this procedure is vital for all imaging data. Building DL models cannot directly use real-world data since it contains noise and missing values in an unprocessed state. Pre-processing procedures for data are required to transform such noisy data into a format that a machine can read. Figure 7 shows various preprocessing techniques for AD detection using DL approaches.

Preprocessing is essential when the quantity of high-quality labeled data is constrained and acquiring additional examples is costly and labor-intensive. Consequently, it should be regarded as a significant concern in medical analysis, especially in the early diagnosis of Alzheimer's disease.

Maintenance of data can be used for tasks like testing and training data. Extraction of data is needed for the two steps above. Taking away the attributes is an easy way to make the data less multidimensional. Direct classification is complicated due to the enormous size and noise of the raw image. Depending on the types of extracted features, input data management solutions may be categorized into four groups: slice-based, voxel-based, regions of interest (ROI)-based, and patch-based (Ahmed et al., 2020; Nguyen and Chu, 2020; Healy and McMahon, 2018). The four primary categories into which it can be separated are shown in Table 3.

The above Section 3 points out that the literature discussing the benefits of diagnosing AD early is based on the opinions of experts rather than research evidence. Not many studies have been published that look at the pros and cons of diagnosing early AD. This may be because AD is only recently defined as a condition that includes both the pre-dementia and dementia stages and the way they were conducted. It's possible that some studies were missed because of the search terms that were used, even though the literature review was thorough and the terms should have been broad enough to include most publications important to a timely diagnosis of AD. Also, some of the studies looked at the possible benefits of noticing cognitive loss early without saying what caused the dementia syndromes. These studies could have included people with other types of dementia or mixed pathology. A different problem with this review is that it didn't rate the scientific quality of the studies. This means that some of them may have had a high risk of bias. Also, because the studies were so different, a full systematic review with meta-analysis could not be done, as required by the PRISMA recommendations.

Several studies looked at the possible economic benefits of diagnosing and treating Alzheimer's disease early. Early identification and treatment are likely to cost more up front, but economic modeling shows that these costs may be balanced out by savings in the long run, mostly from fewer patients having to stay in hospitals and better quality of life for both patients and caregivers. But longitudinal studies are still needed to find out how much it really costs to stop the disease from progressing from predementia to mild to serious AD and to keep people living in their own homes for longer. Because of this, more research needs to be done on the health and social care costs that come with diagnosing AD early, during the prodromal stage.

As previously stated, DL is a branch of ML. Due to its automatic feature extraction, DL is more efficient than previous ML methods. Additionally, DL carries out “end-to-end learning,” in which the network is given tasks and raw data. The important classifiers of DL methods are shown in Figure 8. Compared to other DL techniques, including recurrent neural network (RNN), deep neural network (DNN) autoencoder (AE), and deep belief network (DBN), most studies relied on convolutional neural network (CNN) methods for diagnosing AD using MRI scans. Table 4 lists the advantages and disadvantages of learning and classifying AD diagnostics. The given section discusses AD classification using deep learning methods in detail.

Nearly 90% of studies use ADNI, the most often used dataset in research, alone or in conjunction with other studies. The current go-to data source for studies on AD is ADNI. It was established in 2004 by the National Institutes of Health and the National Institute on Aging with the goal of compiling and organizing data on AD patients, tracking their progression, detecting pathogenesis-related alterations and causes, and finally discovering the origins of the disease and developing a therapy. The four stages of ADNI data are now separated into ADNI-GO, ADNI-1, ADNI-2, and ADNI-3, according to the authors of Dimitriadis et al. (2018). In comparison with ADNI-GO and ADNI-1, which are baseline data, the majority of the information in ADNI-2 and ADNI-3 is monitoring and newly added modal data. The basis of AD is primarily genetic.

To help researchers better understand the genetics behind illness causation, enabling the linking of genomes with neuroimaging and medical evidence is one of ADNI's main objectives. To monitor the onset of MCI and early AD, the main objective of ADNI is to ascertain whether ongoing MRI, PET, and biological samples may be used. Through the Laboratory on Neuroimaging (LONI), Image and Data Archive (IDA), a safe research data repository, all ADNI data are accessible without restriction. To conduct research, disseminate knowledge, or organize clinical research trials, interested scientists may have access to ADNI imaging, clinical, genomic, and biomarker data. Access is subject to compliance with the ADNI data use agreement and the policies indicated for the publications. Through the IDA, data from several dementia studies that supplement ADNI are also accessible. Among them are the ADNI project and the AIBL study, which examine the impact of traumatic brain injury and post-traumatic stress disorder on AD in veterans.

OASIS-3 and OASIS-4 were recently provided by OASIS, an organization that aims to give the scientific community open access to neuroimaging datasets. Pre-published data for OASIS-cross-sectional (Marcus et al., 2007) and OASIS-longitudinal (Marcus et al., 2010) have been utilized in hypothesis-driven data analysis, neuroanatomical atlases, and segmentation algorithm development. The OASIS-3 dataset for normal aging and AD is a longitudinal multimodal neuroimaging, clinical, cognitive, and biomarker dataset. OASIS-4 contains MR, clinical, cognitive, and biomarker data for people who complained about their memory (Khagi et al., 2019).

The OASIS initiative, which comprises two large datasets, aims to provide open brain MRI data, according to the authors of Prasath and Sumathi (2024). The cross-sectional data collection comprised 416 people aged 18–96 with MRI results (people with dementia who are young, middle-aged, and old). For each MRI scan, a minimum of three to four high-resolution-to-noise ratio T1-weighted images were obtained. Intracranial volume measurements and total brain capacity were employed to investigate AD and normal aging (Bachman et al., 2014). The collection also contained information on 20 individuals who had dementia. The xnat.org central The OASIS datasets provide the public with free access to a substantial neuroimaging and processed imaging data source spanning a broad range of demographic, cognitive, and genetic characteristics, together with a user-friendly platform for usage in.

The largest study, the AIBL initiative, uses PET scans. Participants also undergo a variety of other scans, such as a PET to evaluate brain function, a series of MRI contrasts for anatomical characterization (T1w, PDw, and T2w), the structural integrity of the white matter, and pathological imaging, in addition to the PET. MRI is a potent technique for capturing various brain tissues with excellent anatomical detail. Because MRI can evaluate the cortical atrophy linked to neurodegeneration, it has emerged as a crucial method for diagnosing AD. The accurate delineation of the outer cortical mantel is made possible, particularly by relatively quick T1w scans that provide excellent contrast between white and gray matter.

AIBL aims to pinpoint the biomarkers, cognitive characteristics, and lifestyle and health variables that impact the development of symptomatic AD. This includes biopsies, PET, MRI, clinical and cognitive tests, and lifestyle assessment (Martins et al., 2018). In the dataset, about 2,000 people's personal details are contained. The data was gathered using a variety of clinical procedures and surveys. Over a 10-year period, the entire data set was obtained, which included patient records for 582 normal individuals, 142 AD, and 220 MCI. Furthermore, the baseline cohort includes data on 786 healthy individuals, 133 MCI, and 211 AD. The recruitment times, gender, and age were used as proxy data.

Initial funding for the IBSR came from the National Institute of Neurological Disorders and Stroke. Researchers from Boston University, Draper Laboratory, North Eastern University, Massachusetts Institute of Technology, Massachusetts General Hospital and Harvard Medical School use this award to research MR brain segmentation. Since the old website is no longer fully functional (Martins et al., 2018), all data will be made available at the respective website address.

After positional adjustment, the three-dimensional coronal T1-weighted medical examination is the MRI data. Often referred to as “outlines,” the segmentation files result from labor-intensive, semi-automated processes that require many hours of expertise from trained individuals. While manual segmentations aren't quite “ground truth,” they are a good place to start when contrasting different automated segmentation methods.

The IBSR offers carefully guided findings for expert segmentation and data from magnetic resonance brain images (Fatima et al., 2022). Its objective is to further segmentation technique analysis and progress. The ground truth is made up of 20 real T1-weighted (T1w) MRI pictures with carefully focused expert segmentation findings. Additionally, each MRI volume includes ~60 3.1 mm resolution coronal T1w slices and 18 1.5 mm resolution cortical T1w scans (slice gap between succeeding slices). With voxels separated at frequencies of 0.84 × 0.84 × 1.5 mm³ to 0.94 × 0.94 × 1.5 mm³ to 1.0 × 1.0 × 1.5 mm³, the topic volumes in this dataset have a size of 256 × 256 × 128 pixels. A manual division of 32 noncritical structures has also been performed.

The main objective of the MICCAI BraTS'21 challenge is to segment intrinsically heterogeneous brain glioma sub-regions in MRI scans using multi-institutional foundation multi-parametric magnetic resonance imaging (mpMRI) data. A significant pool of routine multi-institutional clinically acquired mpMRI scans of gliomas with a pathologically verified diagnosis and accessible O(6)-methylguanine-DNA methyltransferase promoter methylation status was used to generate the testing, training, and validation. Specifically, the data set used for this year's challenge has received a significant addition of routinely collected mpMRI images since BraTS'20. The validation, training, and testing datasets contain information about a single subject; experienced neuroradiologists produce and approve ground truth annotations of the tumor sub-regions to quantitatively evaluate the predicted tumor segmentations of Task 1. Clinical data is used to assess Task 2.

The MICCAI-2012 dataset (Landman and Warfield, 2012) includes 134 manually segmented structures from 35 T1w MRI volumes received from Scotts Valley, California-based Neuro-Metrics, Inc. The segmentation of tissue, tumors, and structures is its principal application. In 2012, this dataset began with 80 authentic and artificial examples. The amount of training and testing data has grown over time. Subcortical structure segmentation is accomplished using the MICCAI 2012 challenge in multi-atlas labeling. The study simulated various methods for accurately recognizing AD using brain MRI data. The ablation study considers how each contribution affects the effectiveness of the measures. The performance metrics of the suggested methods are contrasted with those of alternative strategies in the following section.

This research study examines diverse methodologies to tackle the identified research issues in order to combat this global sickness. Investigation into effective and precise early detection of Alzheimer's disease is essential to facilitate timely treatment alternatives. Automated methods are necessary to manage the substantial volume of patients' medical imaging data. The fundamental etiology of the disease remains unidentified, with the exception of a limited number of family instances attributable to genetic mutation. At present, there is an absence of therapeutic interventions for the disease, necessitating a solution for the substantial amounts of imaging data required to manage a significant number of patients. A crucial viewpoint is the examination of longitudinal data. Alzheimer's disease is a degenerative condition that develops gradually, and documenting the dynamic alterations is essential for comprehending its progression.

Moreover, it is essential to tackle the obstacles related to restricted data accessibility in Alzheimer's disease research. Deep learning methodologies frequently necessitate substantial quantities of labeled data for peak efficacy. Nevertheless, AD datasets are generally constrained by the challenges and expenses associated with data acquisition.

Future research should concentrate on advancing strategies to improve the transparency and explainability of deep learning models in Alzheimer's disease diagnosis and prediction. Interpretability in medical applications is essential for fostering clinician trust and acceptance, as well as ensuring the ethical implementation of AI technologies. It is essential to include interpretable elements, such as attention mechanisms or saliency maps, into deep learning frameworks for Alzheimer's disease analysis. It is advisable to establish uniform benchmarks and evaluation criteria for deep learning activities linked to Alzheimer's disease to foster collaboration and expedite advancements in the field. This would enable equitable comparisons among various models and promote the repeatability of research results. Moreover, the dissemination of meticulously curated and annotated datasets helps mitigate the challenges posed by data scarcity and promote the advancement of innovative algorithms and approaches.

To address this difficulty, researchers may investigate transfer learning methodologies, wherein pre-trained models on analogous tasks or datasets are refined for Alzheimer's disease analysis. Furthermore, data augmentation techniques can artificially enhance the volume and diversity of accessible data, facilitating the development of more robust and generalizable models. Deep learning models ought to persist in investigating the integration of diverse data sources, including neuroimaging, genetics, and clinical information. Utilizing the complimentary information from different modalities can improve the precision of Alzheimer's disease diagnosis, prognosis, and treatment response prediction. The integration of multimodal data can yield a more holistic understanding of the condition and facilitate the formulation of individualized treatment approaches. Deep learning architectures possess significant potential for enhancing our comprehension of Alzheimer's disease and refining diagnosis, prognosis, and treatment. Integrating various modalities, evaluating longitudinal data, resolving data restrictions, improving interpretability, and promoting collaboration can facilitate the development of more accurate, efficient, and interpretable deep learning models in Alzheimer's disease research. These initiatives possess the capacity to revolutionize clinical practice and aid in the formulation of individualized and targeted interventions for those at risk of or impacted by Alzheimer's disease.