Classification of dementia from FDG-PET parametric images using data mining

Wen, Lingfeng; Bewley, Michael; Eberl, Stefan; Fulham, Michael; Feng, Dagan

doi:10.1109/isbi.2008.4541020

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…A batch normalization layer and an ReLU activation function were added after each convolution layer. We set all convolutional layer strides to 2 and padding was set to be the same as layer (8) input. The structure of 3DCNN for sMRI and PET The structure of 3DCNN used to extract MRI and pet features is the same, but they do not share parameters.…”

Section: Experiments Settingsmentioning

confidence: 99%

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Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

Tang,

Xiong,

Tong

et al. 2024

BioMed Eng OnLine

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Purpose Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer’s disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging. Methods The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD. Results The model attained a noteworthy classification accuracy of 98.1% for Alzheimer’s disease (AD) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area. Conclusions A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.

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Section: Experiments Settingsmentioning

confidence: 99%

“…The PET can monitor the changes in glucose metabolism in the human body [7]. Wen et al [8] extracted PET image features and identified AD from healthy controls by logistic regression. For the features of a single modality, the observed feature information usually only is provided from a certain perspective.…”

mentioning

confidence: 99%

Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

Tang,

Xiong,

Tong

et al. 2024

BioMed Eng OnLine

View full text Add to dashboard Cite

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GA and AdaBoost-Based Feature Selection and Combination for Automated Identification of Dementia Using FDG-PET Imaging

Xia

Zhang

Wen

et al. 2012

Intelligent Science and Intelligent Data Engineering

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Exploitation of 3D Stereotactic Surface Projection for automated classification of Alzheimer's disease according to dementia levels

Ayhan

Benton

Raghavan

et al. 2010

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Alzheimer's disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approach used for determining dementia ratings, which uses a combination of clinical assessments such as memory tests. In this study, we compare Naïve Bayes (NB), a probabilistic learner, with variations of Support Vector Machines (SVMs), a geometric learner, for the automatic diagnosis of Alzheimer's disease. 3D Stereotactic Surface Projection (3D-SSP) is utilized to extract features from PET scans. At the most detailed level, the dimensionality of the feature space is very high, resulting in 15964 features. Since classifier performance can degrade in the presence of a high number of features, we evaluate the benefits of a correlationbased feature selection method to find a small number of highly relevant features.

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Classification of dementia from FDG-PET parametric images using data mining

Cited by 6 publications

References 18 publications

Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

GA and AdaBoost-Based Feature Selection and Combination for Automated Identification of Dementia Using FDG-PET Imaging

Exploitation of 3D Stereotactic Surface Projection for automated classification of Alzheimer's disease according to dementia levels

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