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



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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© 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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