Abstract
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
Highlights
Alzheimer’s disease (AD) is the degenerative disease that is most commonly known and progresses steadily
A direct comparison among outcomes of the different studies is impacted by several parameters such as different datasets as well as different cohort sizes and different neuroimaging modalities; some researchers considered only one modality (MRI), and few of the researchers opted for multimodalities for their research work and different preprocessing techniques as well as different data handling methods
We discussed the method adopted for AD classification while using convolutional neural network (CNN) and what kinds of datasets were available publically, what type of neuroimaging data modalities were available, what sort of preprocessing methods were used, and what sort of data is inputted into the CNN
Summary
AD is the degenerative disease that is most commonly known and progresses steadily. The age-by-age prevalence rate has been growing over the years, and interest in dementia-related research has grown worldwide. AD is one of the most well-known diseases among the old populace, and it confers adverse symptoms of dementia, including problems of memory (like intuition, recollecting, arranging, and judgment) [1]. The reported incidence rate is around 2 percent of the total at 65 years of age and 35 percent of the total or above at the age of 85. It is predicted that by 2050, this number would reach 0.1 billion [2] It leads the hippocampus and cerebral cortex to reduce and the cerebral ventricles to enlarge. The intensity of all these disruptions depends on the stage of the disease. There are similar subgroups of MCI that are less addressed in previous studies, and those subgroups are early MCI (i.e., eMCI) and late (i.e., lMCI) [5]
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