Alzheimer’s disease classification using features extracted from nonsubsampled contourlet subband-based individual networks

Feng, Jinwang; Zhang, Shaowu; Chen, Luonan; Xia, Jie

doi:10.1016/j.neucom.2020.09.012

Cited by 28 publications

(7 citation statements)

References 53 publications

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“…A series of diseases caused by population aging, such as Alzheimer's disease (AD), have become a threat to human health. AD is a neurological condition that worsens with time 1 and causes cognitive decline and behavioral problems 2 . Based on clinical symptoms, it can be categorized as Moderate Cognitive Impairment (MCI), Normal Control (NC), and AD.…”

Section: Introductionmentioning

confidence: 99%

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Miao,

Xu,

et al. 2023

Int J Imaging Syst Tech

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Alzheimer's disease (AD) is a severe neurodegenerative disease that can cause dementia symptoms. Currently, most research methods for diagnosing AD rely on fusing neuroimaging data of different modalities to exploit their heterogeneity and complementarity. However, effectively using such multi‐modal information to construct fusion methods remains a challenging problem. To address this issue, we propose a multi‐modal multi‐scale transformer fusion network (MMTFN) for computer‐aided diagnosis of AD. Our network comprises 3D multi‐scale residual block (3DMRB) layers and the Transformer network that jointly learns potential representations of multi‐modal data. The 3DMRB with multi‐scale aggregation efficiently extracts local abnormal information related to AD in the brain. We conducted five experiments to validate our model using MRI and PET images of 720 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed network outperformed existing models, achieving a final classification accuracy of 94.61% for AD and Normal Control.

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Section: Introductionmentioning

confidence: 99%

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Miao,

Xu,

et al. 2023

Int J Imaging Syst Tech

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“…Machine learning (ML) techniques are attracting substantial interest in the medical field, where deep learning-based models have been successfully utilized in many healthcare applications such as depression detection [15] , pain estimation [16] , breast cancer detection [17] , Alzheimer’s disease classification [18] , and pneumonia detection from chest X-ray images [19] . Due to the increase in COVID-19 cases, healthcare systems have been overwhelmed and require alternative solutions for the automated diagnosis of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Muhammad

Hoque

Oussalah

et al. 2022

Knowledge-Based Systems

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COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

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“…In the early attempts, traditional statistical methods based on voxel-based morphology (VBM) were introduced to measure the brain's morphologic changes. VBM-based studies determine the intrinsic characteristics of specific biomarkers, such as the hippocampus volumes (Fuse et al, 2018 ), cortex sickness (Luk et al, 2018 ), subcortical volumes (Vu et al, 2018 ), and frequency features with non-subsampled contourlets (Feng et al, 2021 ), to calculate the regional, anatomical volume of the brain. However, most VBM-based approaches relying on domain knowledge and expert's experience need a complex handcrafted feature extraction procedure, which is independent of the subsequent classifiers, resulting in potential diagnostic performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network

Chen

Qiao

Zhu

2022

Front. Aging Neurosci.

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Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN). Specifically, we begin by extracting the local slice-level characteristic in various dimensions using multiple sub-networks. Then we proposed a slice-level attention mechanism to emphasize specific 2D-slices to exclude the redundancy features. After that, a 3D-CNN was employed to capture the global subject-level structural changes. Finally, all these 2D and 3D features were fused to obtain more discriminative representations. We conduct the experiments on 1,451 subjects from ADNI-1 and ADNI-2 datasets. Experimental results showed the superiority of our model over the state-of-the-art approaches regarding dementia classification. Specifically, our model achieves accuracy values of 91.1 and 80.1% on ADNI-1 for AD diagnosis and mild cognitive impairment (MCI) convention prediction, respectively.

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Alzheimer’s disease classification using features extracted from nonsubsampled contourlet subband-based individual networks

Cited by 28 publications

References 53 publications

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network

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