Deep sequence modelling for Alzheimer's disease detection using MRI

Ghahnavieh, Amir Ebrahimi; Luo, Siwei; Chiong, Raymond

doi:10.1016/j.compbiomed.2021.104537

Cited by 67 publications

(32 citation statements)

References 42 publications

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“…The proposed model gave a testing accuracy of 95.31%. The study [28] employed multiple deep sequence-based models using 3D ResNet18 with data augmentation Resnet18 to extract features for accurate AD classification and achieved a classification accuracy of 96.88%.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.

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Section: Related Workmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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“…Most of the existing methods [18]- [21] are disease and modality-specific, and optimized for a limited number of data samples. Moreover, these methods are designed to make diagnostic decisions based on 2D imaging data employing image-based classification models [22]- [31], even in case of 3D imaging data.…”

Section: A Potential Research Gaps and Motivationmentioning

confidence: 99%

Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

Owais

Cho

Park

2023

IEEE Trans. Ind. Inf.

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The automatic and accurate classification of medical imaging data has potential applications in computer-aided disease diagnosis, prognosis, and treatment. However, it remains a challenge to optimize recent deep learning algorithms in medical domain for the accurate classification of large-scale 3D volumetric data. To address these challenges, we propose an efficient deep volumetric classification network based on the aggregation of multilevel deep features for accurate classification of large-scale medical 2D/3D imaging data. To perform a detailed quantitative analysis of our method, 26 different datasets were fused to construct a single large-scale multimodal database that comprises a total of seventy different classes, including 151,095 data samples. Additionally, 15 different baseline methods were configured under the same experimental protocol for volumetric model genesis and extensive performance comparison with our method. The experimental results of our method exhibited promising performance as area under the curve of 93.66% and outperformed various state-of-the-art methods.

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“…The sliced-based techniques [20], [21] extract slices from the 3D neuroimaging brain scan by projecting the sagittal, coronal, and axial to the 2D image slices. Indeed, because non-affected regions and normal slices must be chosen as the reference distribution, they cannot account for the disease and may be considered an anomaly [22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, different pretrained deep learning models on ImageNet, such as DenseNet, VGG16, GoogLeNet, and ResNet, can be fine-tuned by 2D slices to classify AD from CN [19]. In [21], researchers extracted features from MRI image slices using a pre-trained 2D CNN and fed the extracted feature sequence to a recurrent neural network (RNN). The RNN was in charge of determining the relationship between the sequence of extracted features corresponding to MRI image slices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The coarse predictions were then used in ensemble learning to obtain refined results for all slices. Ebrahimi et al [21] extracted a sequence of features from 2D MRI slices using a pre-trained ResNet18 [33], which they subsequently trained a temporal convolutional network and several types of RNNs to classify AD/CN. Zhao et al [24] introduced a region ensemble model with three sequential sub-networks to account for a global feature map derived from the entire brain and regional feature maps extracted using a segmentation model.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer’s Disease Using Structural MRI Images

Ashtari-Majlan

Seifi

Dehshibi

2022

IEEE J. Biomed. Health Inform.

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Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.

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Deep sequence modelling for Alzheimer's disease detection using MRI

Cited by 67 publications

References 42 publications

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer’s Disease Using Structural MRI Images

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