2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2018
DOI: 10.1109/cibcb.2018.8404980
|View full text |Cite
|
Sign up to set email alerts
|

A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data

Abstract: Alzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers.In this paper, convolutional neuronal network architecture AlexN… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
32
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 50 publications
(32 citation statements)
references
References 22 publications
0
32
0
Order By: Relevance
“…The superiority of the proposed approach as compared to the conventional ML approach was authenticated in terms of accuracy. CNN-AlexNet was used in [70] to classify the processed fMRI data into 5 categories naming NC, significant memory concern, EMCI, LMCI, and AD. A good number of preprocessing of the raw data including removal of unwanted tissues, slice timing corrections, spatial smoothing, high pass filtering, and spatial normalization resulted in very high accuracy of detection by AlexNet.…”
Section: ) Dl-based Approaches In Ad Diagnosismentioning
confidence: 99%
“…The superiority of the proposed approach as compared to the conventional ML approach was authenticated in terms of accuracy. CNN-AlexNet was used in [70] to classify the processed fMRI data into 5 categories naming NC, significant memory concern, EMCI, LMCI, and AD. A good number of preprocessing of the raw data including removal of unwanted tissues, slice timing corrections, spatial smoothing, high pass filtering, and spatial normalization resulted in very high accuracy of detection by AlexNet.…”
Section: ) Dl-based Approaches In Ad Diagnosismentioning
confidence: 99%
“…A number of studies have leveraged the power of ML methods to build flexible nonlinear mapping models and use them to identify neural correlates of brain disorders (e.g., Hasanzadeh et al, 2019;Kazemi & Houghten, 2018;Kim et al, 2016;Leming et al, 2020) and behavioral traits (e.g., Kumar et al, 2019;Morioka et al, 2020;Xiao et al, 2019). Yet the vast majority of cognitive neuroscience studies use linear mapping models (such as linear regression), resulting in a gap between different neuroscience subfields.…”
Section: The Controversymentioning
confidence: 99%
“…The concurrent increase in the size of available datasets (e.g., Chang et al, 2019;Majaj et al, 2015;Schoffelen et al, 2019) has enabled researchers to train large-scale mapping models without overfitting them. As a result, a number of applied neuroscience studies have leveraged the power of ML-based methods to build flexible nonlinear mapping models and use them to identify neural correlates of brain disorders (e.g., Hasanzadeh et al, 2019;Kazemi & Houghten, 2018;Kim et al, 2016;Leming et al, 2020) and of behavioral traits (e.g., Kumar et al, 2019;Morioka et al, 2020;Xiao et al, 2019).…”
Section: The Controversymentioning
confidence: 99%
“…The derivative to calculate gradient direction is denoted in Eq. (5). For example H is 0 for a vertical edge which is darker on the right side.…”
Section: Prewitt Kernelmentioning
confidence: 99%
“…Moreover, the selection of appropriate modality based images is also a significant task for obtaining required information for successful pathological analysis. Positron Emotion Tomography (PET), ultrasound, and many derivative protocols of MRI [5] (diffusion-tensor MRI and fMRI) are used to retrieve information of physical structures with respect to metabolic activities. In the field of pathological image analysis, image accusation, pathological region identification (segmentation) [6], pathological feature extraction [7], significant feature selection [8], and model training (classification) are the main research areas and each contains sub-parts as well.…”
Section: Introductionmentioning
confidence: 99%