Diagnosis of Alzheimer’s Disease Using Brain Imaging: State of the Art

  • Atif ShahEmail author
  • Kamal Niaz
  • Moataz Ahmed
  • Reem Bunyan


Alzheimer’s disease (AD) is one of the prominent diseases in elderly people which leads to language impairment, disorientation, memory loss, and eventually death. Despite the severity of the disease, there is no such drug reported to control, reduce, or stop the progression of AD. The neuroimages played a crucial role in tracking the progression of AD using biomarkers which help the physicians to diagnose the disease more accurately. In this study, we investigate the effectiveness of structural and functional neuroimaging modularities which are used in the state-of-the-art methods to diagnose AD. The finding shows that most of the studies prioritize magnetic resonance imaging techniques (MRIT) solely or combined with other neuroimaging modularities to achieve better performance. Studies also founded that only few public datasets are available, and the most widely used public dataset is Alzheimer’s Disease Neuroimaging Initiative.


Medical imaging MRI Alzheimer’s disease CAD Features extraction 



Three dimensions




Alzheimer’s disease


Alzheimer’s Disease Neuroimaging Initiative


Area under the curve



Computer-aided diagnosis


Convolutional neural network




Cerebrospinal fluid


Computed tomography


Deep learning


Diffusion tensor imaging


Diffusion-weighted imaging


Functional connectivity




Functional magnetic resonance imaging






Gaussian process logistic regression


Independent component analysis


Local linear embedding


Mild cognitive impairment


Amnestic MCI


MCI converted


MCI non-converted


Progressive MCI


Stable MCI


Magnetic resonance imaging


Magnetic resonance spectroscopy


N-Acetyl aspartate


Principal component analysis


Positron emission tomography


Radial basis function


Resting-state fMRI


Recursive feature elimination


Receiver operating characteristic


Reactive oxygen species






Single photon emission computer tomography


Support vector machine










Voxel-based measure


White matter


White matter hyperintensities

7.1 Introduction

Alzheimer’s disease (AD) is the utmost common cause of cognitive impairment usually in elderly people; around 11% of people at the age of 65 and above suffered from AD (Alzheimer’s Association 2014). AD progression leads to memory loss, disorientation, and language impairment and eventually leads to death, which increased to 123% between 2000 and 2015 (Alzheimer’s disease facts and figures 2018). It is well understood that the prevalence and severity of AD are high, still there is no drug or treatment that has been reported to reduce the risk or completely stop AD progression. The impact of AD can be understood via the progressive brain changes associated with AD. To achieve this, brain imaging played a profound role in track changes in the follow-up scans using different imaging techniques and transformed AD research and clinical practices. Brain imaging provides informative biomarker even before clinical symptoms have appeared. These biomarkers help in the diagnostic decision and disease assessment and improve diagnosis. The use of a biomarker for AD will also be helpful for clinical trials because of its enrichment strategies or tough inclusion criteria.

Imaging biomarker played an important role in AD detection and its early stage of mild cognitive impairment (MCI) using structural and functional neuroimaging. Recent diagnostic criteria (McKhann et al. 2011) suggest that neuroimaging biomarkers perform better in monitoring AD progression and its early detection than other biomarkers. Different imaging modularities have been used, including computed tomography (CT), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single photon emission computer tomography (SPECT), diffusion-weighted imaging (DTI), and magnetic resonance spectroscopy (MRS), for biomarkers for detecting AD and predicting its progression. These biomarkers used features such as voxel-wise tissue probability (Kloppel et al. 2008; Liu et al. 2014; Tong et al. 2014), cortical volumes (Wolz et al. 2011; Zhang et al. 2011; Zhou et al. 2011), and thickness (Eskildsen et al. 2013; de Magalhaes Oliveira et al. 2010; Querbes et al. 2009) for classification of AD and MCI.

In this study, we investigate various structural and functional brain imaging techniques to detect AD from HC or MCI individuals. The AD can be detected via its progressive behavior which is illustrated in Fig. 7.1, showing the healthy-control/normal-control (CH/CN), MCI, and AD patient MRI with 2-year follow-up images and their differences. The biomarkers which are automatically extracted and classify AD, HC, and MCI patients are called computer-aided diagnosis (CAD). In this chapter, we discussed the relationship of AD with brain imaging, various types of structure and function neuroimaging techniques, and the observations along with conclusion and future directions.
Fig. 7.1

MRI shows the extracted brain of HC, MCI, and AD patient with 2-year follow-up and their differences (Ledig et al. 2018). (The image is reproduced under the Creative Commons license)

7.2 Relationship of Brain Imaging with AD

AD is a frequent form of neurodegenerative disorder across the world, which has become a public health problem. And imaging plays an important role in the diagnosis of AD and dementia with a special focus on CT, MRI, fMRI, and PET (Johnson et al. 2012). Many of the studies revealed that computer imaging is converted from minor exclusionary role to central position which provides information of temporal and spatial revolution in AD (Selkoe et al. 2012; Krstic and Knuesel 2013). Epidemiological studies indicated that aging and hereditary predisposition are the two key risk factors for AD (Omar et al. 2017). On the other hand, pathological studies of AD have characterized extracellular aggregation of senile plaques (SPs) and development of intracellular neurofibrillary tangles and lesions of cholinergic neurons. Also, molecular pathology such as amyloid deposits can be visualized via imaging in AD (Johnson et al. 2012; Wu et al. 2011). In the central cholinergic system, numerous neurotransmitters and neuronal pathways function together in learning and memory. Thus, the cognitive impairment in AD is related to functional loss in the central cholinergic system (Palle and Neerati 2017). Moreover, aggregation of amyloid-β (Aβ) peptide, which is referred to as amyloid hypothesis, is found to cause synaptic dysfunction and neurodegeneration (van Dyck 2018). These amyloid plaques can’t be seen through structural MRI which cannot detect histopathological trademarks of AD (Johnson et al. 2012). Other pathological processes are also reported in relation to AD such as neuroinflammation, impairment of cerebral circulation, altered synaptic function, and cerebral amyloid angiopathy. Disruption of default network activities during sleeping and resting stage has been revealed via PET imaging (Sperling et al. 2009; Hedden et al. 2009). Hence, these pathological processes that are identified to cause AD are considered important drug targets toward AD drug search. However, still further multi-modality studies are needed to explore the correlationship between MRI, fMRI, fluorodeoxyglucose (FDG)-PET, and PET imaging techniques and AD (Jack et al. 2010; Ghanemi 2015). Considerable number of evidence indicates that dietary control can minimize the risk of developing AD. Animal model studies have also shown a decreasing high-calorie diet to be neuroprotective by reducing Aβ accumulation (Hartman et al. 2006). Generally, flavonoids and in particular quercetin are important compounds for the development of AD therapeutics since it can protect the neurons against oxidative agents and excitotoxicity through regulating cell death mechanisms. FDG-PET is the vital biomarker to detect expression of AD-specific genes, mitochondrial dysfunction, neuropathy, oxidative stress, glial excitation, and synapse loss (Johnson et al. 2012) (Ansari et al. 2009). Oxidative stress reflects an imbalance between reactive oxygen species (ROS) production and biological system’s antioxidant defense mechanisms which act by detoxifying or repairing the reactive intermediates, thereby causing damage. These damages can be seen via PET, ADNI FDG data analysis, fMRI, and MRI (Johnson et al. 2012; Chen et al. 2010; Dong et al. 2014). ROS act as the neurotransmitters and excitatory amino acids in the brain and neuronal tissue. In addition to that, the brain itself represents a substantial source of oxidative stress, as its metabolism serves as a “factory” of ROS which attacks glial cells and neurons, resulting in neuronal damage which may lead in oxidative injury and programmed cell death by apoptosis which is evaluated via computer imaging (Attwell et al. 2010; Hanisch and Kettenmann 2007; Iadecola 2004; Bezzi and Volterra 2001). All these studies revealed that brain imaging/computer imaging is an important tool to explore the histopathological condition of AD. MRI and CT are used to illustrate structural neuroimaging and SPECT, PET, FMRI, and DTI for functional neuroimaging, respectively, which represent the CAD system as shown in Fig. 7.2.
Fig. 7.2

An abstract level of AD classification framework using neuroimaging

7.3 Brain Imaging in AD Diagnosis

7.3.1 Structural Neuroimaging Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a medical imaging technique used to measure the hydrogen atoms’ radiofrequency energies. During the static magnetic field, the nuclei with higher energy align against the field, while the lower energy aligns with the field. However, with a specific frequency, the low-energy nuclei absorb energies and align against the field. With the discontinuation of radiation, the MRI scanner detects the emitted energies, and the nuclei return to their lower-energy state. Such changes in energy levels can provide brain structures in the form of detailed images: T1-weighted (T1-w) and T2-weighted (T2-w). T1-w distinguishes gray and white matter, while T2-w characterizes white matter hyperintensities.

MRI is one of the major imaging techniques used to evaluate AD in clinical studies (Byun et al. 2017; Boutet et al. 2014; McEvoy and Brewer 2010) as well as in CAD methods, where computers detect AD automatically using machine learning algorithms (Kloppel et al. 2008; Liu et al. 2014; Tong et al. 2014; Wolz et al. 2011).

Beheshti and Demirel (2016) proposed a feature ranking-based method to classify AD and healthy-control (HC) individuals. Voxel-based morphometry was used to compare the global and local differences, a significant difference was extracted called a volume of interest (VOI), each voxel in VOI is used as a feature, t-test score was used to rank the features, and fisher criterion was used to select the dominant features with support vector machine (SVM) classifier. The work extended (Beheshti et al. 2017) where the same author used the feature ranking based on a genetic algorithm to optimize the feature selection process. In their another work, feature ranking was used with classification error to identify AD patients (Beheshti et al. 2016). Seven different ranking methods were used which include information gain, statistical dependency, t-test score, Pearson correlation coefficient, Gini index, Fisher’s criterion, and mutual information. The classification error was used to select the discriminatory features during the training phase. SVM was used as a classifier with tenfold cross-validation.

Eskildsen et al. (2015) proposed a novel method to predict AD using T1-w MRI. The mutual information fusion was used to identify the robust features which were stable for the period of 3 years. Five features were used to classify AD and MCI subjects which include left and right hippocampi grading, the right anterior part of the parahippocampal gyrus, and the cortical thickness of the left precuneus and left superior temporal sulcus. These features were used in linear discriminant classifier using leave-one-out cross-validation to predict AD and MCI subjects.

Cuingnet et al. (2011) used ten different techniques for CN, AD, and MCI classification. These methods include five voxel-based methods, two methods using the hippocampus, and three methods based on cortical thickness. The results were compared: CN vs AD, CN vs MCI, and MCI non-converted vs MCI converted. This work also evaluated the time complexity of hyper-parameter tuning and feature selection. Westman et al. (2012) used the combination of MRI and cerebrospinal fluid (CSF) measures to detect AD and MCI conversion. In this study, 34 cortical thickness, 34 subcortical volumetric, and 3 CSF measures with a total of 699 (AD = 187, MCI = 287 and CTL = 225) measures were used for classification. The experiments were performed on AD vs HC, MCI vs HC, and MCI vs AD prediction.

Liu et al. (2013) used an unsupervised method, the local linear embedding (LLE) algorithm. The algorithm is used to transform the MRI data to local linear space with few dimensions. These features were used to train a classifier for AD prediction. The work demonstrated that LLE feature performed better in classification than using direct features. Moradi et al. (2015) used the semi-supervised method to detect the conversion of MCI to AD in MCI subjects. The low-density separation method was used to extract features and integrated with cognitive and age measures to improve the performance. The results showed that the combination of these features improved the area under the curve (AUC).

Sørensen et al. (2017) used cortical thickness hippocampal texture, shape, and volumetry as features to detect CN, AD, and MCI patients. The work secured first place in CAD-dementia challenge where it evaluated on two datasets, Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging Biomarkers ad Lifestyle Study of Ageing (AIBL), with tenfold cross-validation. Linear discriminant analysis was used for feature reduction. CT

CT is a diagnostic imaging test which draws the detailed images of internal organs such as bone, vessels, and soft tissues. It is a computer-processed combinations of many X-ray imaging procedure where the rotated beam of X-rays is pointed to the body to make cross-sectional images, also called slices, which can be visualized in three dimensions (3D). The attenuation of the body tissues varies which is why the highest attenuation appears in white such as bones, while other tissues appear in black. That’s why CT has the ability to visualize fluid, gas, and soft tissues. However, it cannot differentiate the gray and white matter (G and WM). To detect AD in CT images, boundary detection is one of the important methods which can increase the detection rate. Al-Jibory and El-Zaart (2013) proposed a method based on Weibull distribution instead of Gaussian distribution to detect boundary detection. The advantages of this method are to address more challenging problem where the distribution has both symmetric and asymmetric shapes.

7.3.2 Functional Neuroimaging SPECT

SPECT is a type of nuclear imaging which is used to view how the blood flows in brain arteries and veins. Radioactive materials are used before SPECT scans, called tracer. These materials emit a single photon which is detected by the machines and translated to two-dimensional cross sections. These cross sections are then converted to 3D brain scans (Schuckit 1992). In comparison with PET, SPECT cannot determine glucose metabolism and also have lower resolution in imaging deep structures. Tabei et al. (2017) used SPECT and white matter hyperintensity (WMH) to predict cognitive decline, which is one of the major problems in AD patients. The study was conducted on 182 patients which concludes that regional cerebral blood flow and WMH are the main parameters that affect the cognitive function in AD patients. PET

PET also uses tracers which emit positron. When a positron encounters with the electron, it gets destroyed and releases a photon which travels in opposite direction. The scanner detects the arrival of two photons simultaneously and determines the travel direction of photons. The scanner then uses this information to contract the PET image just like SPECT (Strauss 1986). However, the PET images have good spatial resolution and are used widely because of lower scanner cost and more availability of PET tracers.

Azmi et al. (2017) used the voxel intensity values of PET images as features to detect AD and HC individuals. The voxel intensities feature consists of mean voxel intensity, slice-based intensity, and global mean voxel intensity. Neural network with nodes 1 and 10 was used for the classification with z-scores. Silveira and Marques (2010) used voxel intensity with a total of 150 features with a weak classifier which combines the outputs to make a complex classifier. Each time the classifier was boosted via re-weighting which improves the performance. The work used AdaBoost algorithm, and for evaluation on selected features, SVM with radial basis function (RBF) kernel was used with tenfold cross-validation. Garali et al. (2015) extracted features from the region of interest and used separation power factor method to select the best 21 regions for further classification which improve the performance and reduce the time complexity. SVM and random forest were used as classifiers. Gray et al. (2012) used multiregional features extracted from PET images. The statistical analysis t-test was conducted to evaluate the proposed method, and two-class SVM was used for classification.

Darsana and Abraham (2016) applied the multistructure registration approach using both PET and MRI images to detect AD and MCI individuals. Asim et al. (2018) applied multi-model and multi-atlas-based method for AD detection using MRI and PET images. Voxel-based measure (VBM), GM, and WM maps were extracted from MRI, while cerebral metabolic rates of glucose were extracted from PET images and used as features. Principal component analysis (PCA) was used to reduce the image features with help of four major steps; normalize image data, calculate covariance matrix from the image data, perform single data decomposition (SVD) and find the projection image data to the new basis with reduced features. SVM with RBF kernel was used for leave-one-out cross-validation. Ota et al. (2015) used Loni probabilistic brain atlas and automated anatomical labeling to extract relative cerebral metabolic rate and gray matter density features. The features were fed to SVM-recursive feature elimination (SVM-RFE) for feature reduction to avoid overfitting and for classification with leave-one-out cross-validation. fMRI

fMRI is used to detect brain activity and changes in blood oxygenation and flow which arise due to the initiation of the activity, also called hemodynamics. The active area of the brain consumes more oxygen which increases blood demand. fMRI produce the activation maps which are represented in different color codes, showing the strength and involvement of specific brain region in a particular mental process. The advantage of this imaging technique is that it does not use any radiation just like CT and PET.

Long et al. (2016) used to detect MCI using SVM-based methods with the multilevel characteristic of MRI. The MCI is the transition phase from CH/HC to AD and an important step for early therapeutic interventions. Hurst exponents, gray matter density, the amplitude of low-frequency fluctuations, and regional homogeneity were used as the main features for SVM with leave-one-out cross-validation. Challis et al. (2015) utilized Gaussian process logistic regression (GP-LR) with SVM using fMRI images to detect CH vs amnestic MCI (MCI-A) and MCI-A vs AD. The number of features was selected via Kendall tau correlation coefficient ranking, and features normalized via PCA and the GP-LR parameters were optimized to increase the accuracy. Leave-one-out cross-validation was used for GP-LR and SVM models.

Khazaee et al. (2017) used local and global graph measure features with SVM and naïve Bayes classifier. Fisher algorithm was used for feature selection based on the discrimination ability, followed by forward-sequential feature selection and tenfold cross-validation for robust classification. Using both the classifiers with optimal features, Bayes classifier performed better using all patient data and achieved higher AUC in HC vs AD classification. Hojjati et al. (2017) extracted graph measures of segregation and integration using graph theory. The study focused on MCI patients where they classify MCI converter (MCI-C) from MCI non-converter (MCI-NC). Global and local graph measures were calculated, which ended with 913 features. Feature selection method was used to sort the features, and sequential feature collection was used to find the optimal features for classification. SVM with leave one out cross-validation was used along the statistical analysis.

Schouten et al. (2016) combined various modalities of MRI and fMRI to classify AD and HC individuals. Various features were extracted including anatomical, diffusion, and functional connectivity features with the elastic net classifier using tenfold cross-validation. In another study (de Vos et al. 2018), eight measures were extracted including functional connectivity (FC) dynamics, FC metric, FC states, graph properties, FC for each voxel, FC for each hippocampus and low-frequency fluctuation, eigenvector centrality, and the combination of all these features. Agosta et al. (2012) explored the salience, default mode, and executive networks to detect the resting-state abnormality in MCI-A and HC. The experiments were conducted on very few images which is one of the limitations of this study. Li et al. (2018) used a transfer learning approach to improve the detection rate on a smaller dataset. ADNI dataset was used for training, and then the trained weights were transferred via adaptation method for Tongji dataset classification which consists of only 12 AD and 14 CN individual’s data. The results improved by 30% than that using the smaller dataset. DTI

DTI is the extension of diffusion-weighted imaging which provides data about WM region orientation. The measurements are based on Brownian motion and water molecules. DTI provides quantitative information to track the magnitude, orientation, and anisotropy of brain WM regions with the help of computer algorithms.

Ahmed et al. (2017) developed a CAD system by extracting features from T1-w MRI and mean diffusivity from DTI images. The AD disease-related signature was generated which was used in multiple kernel learning approach for classification with tenfold cross-validation. Kantarci et al. (2017) used DTI images in clinical perspective to analyze the white matter integrity and pathologic staging in AD. The voxel-based and atlas-based analyses were used for the evaluation of neurofibrillary tangle stages. Schouten et al. (2017) used voxel tensor measures, graph measures, and independent component analysis (ICA)-clustered measures. All features were used for classification independently and also as a fusion vector via elastic net classification with tenfold cross-validation. The results demonstrate that fractional anisotropy with ICA performed better than other measures. MRS

MRS is considered as a noninvasive diagnostic test to estimate the chemical changes in the brain. MRS compares the normal and abnormal tissues, while MRI is used to detect the anatomical location of abnormal tissues. Most commonly used MRS is proton spectroscopy which analyzes the proton molecules. MRS is used to quantify metabolites such as alanine, N-acetyl-aspartate (NAA), creatine (CR), amino acids, and myoinositol.

Proton MRS was used by Wang et al. (2015) to identify the cerebral metabolite changes in AD patients, which shows that NAA was significantly reduced in bilateral hippocampus and posterior cingulate. NAA/CR ratio also decreased in posterior cingulate. Other studies also discovered the reduction of NAA/CR in AD patients (Schuff et al. 1997; Adalsteinsson et al. 2000).

7.4 Observations

In this study, we observed that most of the biomarkers are extracted using MRI, fMRI, and PET, while the rest of the images such as DTI are used by very few studies as shown in Table 7.1. It is well understood that MRI, fMRI, and PET images separately and in combination provide more details to extract biomarkers for AD with better classification rate as shown in Table 7.1. However, it’s unfair to compare the ACC, SEN, and specificity (SPE) of MRI, fMRI, PET, and DTI listed papers because they are not using the same classification pairs or the same datasets, i.e., Beheshti and Demirel (2016) classify CN and AD using MRIs of ADNI dataset, while Garali et al. (2015) classify CN and AD using PET but with a private dataset. Similarly, Silveira and Marques (2010) used PET and ADNI dataset but did the classification of AD and MCI subjects. Table 7.1 also shows that only a few studies used unsupervised or semi-supervised methods (Liu et al. 2013; Moradi et al. 2015), while the rest of the methods used a supervised method with naïve Bayes (Khazaee et al. 2017), neural network (Azmi et al. 2017), and SVM (Long et al. 2016; Hojjati et al. 2017) classifiers.
Table 7.1

Summary of AD detection methods using brain imaging





Results (%)


Healthy-control (HC)/normal-control (CN)

Alzheimer’s disease (AD)

Mild cognitive impairment (MCI)

Beheshti and Demirel (2016)


Feature ranking-based approach



ACC = 96.32

SEN = 94.11

SPE = 98.52

AUC = 0.9993

ADNI (Wyman et al. 2013)

Beheshti et al. (2017)


Feature ranking + genetic algorithm



MCI-S = 65

MCI-P = 71

ACC = 93.01

SEN = 89.13

SPE = 96.80

AUC = 0.9351


Eskildsen et al. (2015)

MRI (T1-w)

Measurements of structural pathologic patterns




ACC = 71.9

SEN = 69.6

SPE = 73.6

AUC = 0.763


Beheshti et al. (2016)


Feature ranking + classification error



ACC = 92.38

SEN = 91.07

SPE = 93.89

AUC = 0.96


Cuingnet et al. (2011)


Comparative analysis of 10 methods



MCI-NC = 67

MCI-C = 37

For CN vs AD:

SEN = 81

SPE = 95


Westman et al. (2012)


MRI measures with combination of CSF measures




ACC = 91.8

SEN = 88.5

SPE = 94.6

AUC = 0.958


Liu et al. (2013)





MCI-S = 93

MCI-C = 97

ACC = 68.0

SEN = 80.0

SPE = 56.0

P = 0.007


Moradi et al. (2015)


Low-density separation



MCI-P = 164

MCI-S = 100

AUC = 0.902


Sørensen et al. (2017)

MRI (T1-w)

Cortical thickness, hippocampal shape, texture, and volumetry

ADNI: 169

AIBL: 88


ADNI: 101

AIBL: 28


ADNI: 234

AIBL: 29


ACC = 63.0

AUC = 0.832

ADNI, AIBL (Ellis et al. 2009), CAD-dementia (Bron et al. 2015)

Azmi et al. (2017)


Voxel intensities + neural network



Nural-net 10 Nodes:

SEN = 83.0

SPE = 94.0

Nural-Net 1 node:

ACC = 90.0

SEN = 81.0

SPE = 95.0


Gray et al. (2012)


Multiregional analysis



MCI-S = 64

MCI-P = 53

ACC = 88.4

SEN = 83.2

SPE = 93.6

AUC = 0.94


Silveira and Marques (2010)


Voxel intensities + AdaBoost




AD detection:

ACC = 90.97

MCI detection:

ACC = 79.63


Garali et al. (2015)


Region-based features



ACC = 95.07

“La-Timone” University Hospital, in the Nuclear Medicine Department (Marseille, France)

Darsana and Abraham (2016)


Multi-structure registration approach




ACC = 96.4


Asim et al. (2018)


Multimodal and multi-atlas-based approach




CN vs AD:

ACC = 94.00

SEN = 95.0

SPE = 93.0


Ota et al. (2015)


Relative cerebral metabolic rate + gray matter density + SVM-RFE


MCI-A = 40

AUC = 0.75

SEAD-J (Ota et al. 2014)

Zhu et al. 2015 (Dong et al. 2014)


Sparse multitasking learning framework



MCI-C = 43

MCI-NC = 56

AD vs NC

ACC = 95.7

SEN = 96.6

SPE = 98.2

AUC = 0.981


Long et al. (2016)


SVM-based method with multilevel characteristic of MRI



ACC = 96.77

SEN = 93.10

SPE = 100

Private dataset

Challis et al. (2015)


Gaussian process logistic regression + SVM



MCI-A = 50


ACC = 75%

SEN = 80

SPE = 70

AUC = 0.7


ACC = 97%

SEN = 90.0

SPE = 90.0

AUC = 0.89

Private Dataset

Hojjati et al. (2017)


Graph theory + SVM

MCI-C = 18

MCI-NC = 62


ROC = 91.4

SEN = 83.24

SPE = 90.1

AUC = 0.95


Khazaee et al. (2017)


Directed graph measures + naïve Bayes classifier




Overall ACC = 93.3


AUC = 0.92


AUC = 0.89

HC vs AD

AUC = 0.94


Agosta et al. (2012)


Default mode + fontal network + salience networks



MCI-A = 12

Default mode network I:

F = 19.52

P < 0.001

Executive Network:

F = 11.19

P < 0.001

Outpatient Memory Clinic of the IRCCS Centro San Giovanni di Dio, Brescia, Italy.

de Vos et al. (2018)


Functional connectivity + independent component analysis



ACC = 79.0

SEN = 86.0

SPE = 71.0

AUC = 0.85

PRODEM (Seiler et al. 2012)

Schouten et al. (2016)

MRI, re-fMRI

Multi-modalities + elastic net classifier



ACC = 93.0

SEN = 81.6

SPE = 95.6

AUC = 0.971


Li et al. (2018)


Transfer learning





With adaptation:

ACC = 84.6

SEN = 92.0

SPE = 79.0

AUC = 0.80

ADNI, Tongji Hospital at Wuhan

Ahmed et al. (2017)


Multimodal approach + multiple kernel learning




AD vs NC

ACC = 90.2

SEN = 82.92

SPE = 97.2


Schouten et al. (2017)


Voxel-wise measures + elastic net classification



ACC = 85.1

SEN = 86.8

SPE = 84.4

AUC = 0.92 ± 0.018


Some of the notations used in this table are accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the curve (AUC), F-score (F), and p-value (P)

Deep learning (DL) is another supervised method which recently got attention due to its state-of-the-art performance in image processing and computer vision. DL also cultivated the area of medical imaging (Klang 2018; Lakhani et al. 2018); the auto-encoder, deep neural networks, and the most widely used 2D and 3D convolutional neural networks (CNN) are focused on detection, segmentation, registrations, content-based image retrieval, image reconstruction, and generation (Litjens et al. 2017). However, in the area of AD detection, DL work is limited and it may be due to the small amount of data. DL algorithms such as CNN need a large amount of data, resources, and time to train the model and tune the hyperparameters. To all such issues, one solution is transfer learning, train the model on large dataset, and transfer the trained weights for small dataset image classification as adopted by Li et al. (2018) for AD small dataset classification. Most of the studies conducted the experiments on ADNI (Wyman et al. 2013) dataset which is the largest AD dataset publicly available. According to ADNI, a total of 800 participants including 200 CH/HC subjects, 400 MCI individuals, and 200 AD subjects are recruited from 50 different places from the United States and Canada.

The evaluation metrics for biomarker used in AD are accuracy, SPE, and sensitivity (SEN) which shows the true-positive (TP) rate and true-negative (TN) rate, respectively, while AUC shows the classifier stability, and receiver operating characteristic (ROC) shows the classifier diagnostic capability. These metrics can be extracted from a confusion matrix which consists of TP, TN, false-positive (FP), and false-negative (FN). The mathematical definitions are as follows:

$$ \mathrm{Accuracy}=\frac{\left|\mathrm{TN}\left|+\right|\mathrm{TP}\right|}{\left|\mathrm{TN}\left|+\left|\mathrm{TP}\right|+\left|\mathrm{FP}\right|+\right|\mathrm{FN}\right|} $$
$$ \mathrm{Sensitivity}=\frac{\left|\mathrm{TP}\right|}{\left|\mathrm{TP}\left|+\right|\mathrm{FN}\right|} $$
$$ \mathrm{Specificity}=\frac{\left|\mathrm{TN}\right|}{\left|\mathrm{TN}\left|+\right|\mathrm{FP}\right|} $$

7.5 Conclusion and Future Perspective

AD, one of the leading neurodegenerative disorders, is characterized by brain damage and cognitive impairment in elderly people. Due to the severity of the disease, prevention and/or regression of AD is a challenge to overcome. To track its progression, medical imaging played a vital role from decades, but still, there is room for improvement. The brain images are used to extract the biomarkers which help the doctors in the identification of AD. In this study, we presented various brain image modularities where the research focused more on MRI due to its clarity, availability, and correctly exposing the biomarkers than other imaging techniques (CT, SPECT, PET, FMRI, and DTI).

In future effort, researchers should consider more datasets with a large amount of imaging data for experimentation to improve the AD detection. Researchers should not only focus on imaging but also take clinical symptoms into consideration and used as features which can help in better identification of AD. DL is a new area which has been enriching in medical imaging. However, DL and transfer learning still have not been exploited to the extent in CAD systems for AD.



The authors wish to acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for utilizing the various facilities in carrying out this research. Other authors of the manuscript thank and acknowledge their respective universities and institutes as well.

Conflict of Interest

There is no conflict of interest.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information and Computer ScienceKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Department of Pharmacology and Toxicology, Faculty of Bio-SciencesCholistan University of Veterinary and Animal Sciences (CUVAS)BahawalpurPakistan
  3. 3.Neurosciences CenterKing Fahd Specialist Hospital DammamDammamKingdom of Saudi Arabia

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