Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Sun, Hao; He, Shanshan

doi:10.3390/ijerph19084508

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…Along with the CNN, few works have incorporated Long Short-Term Memory (LSTM). For instance, Sun et al used a CNN with residuals combined with a multiple LSTM to predict the progression of MCI to AD [16]. Similar to the previous work, Feng et al used a combination of 3D-CNN and FSBi-LSTM [17].…”

Section: Related Workmentioning

confidence: 99%

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

Illakiya,

Karthik

2023

IEEE Access

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The early identification and treatment of Mild Cognitive Impairment (MCI) play a crucial role in managing the risk of Alzheimer's disease (AD). However, current methods for categorizing progressive MCI and stable MCI based on brain MRI scans have proven insufficient due to the subtle nature of the features involved. This research aims to improve the effectiveness of MCI classification through the utilization of a Deep Learning (DL) network. The primary objective of this work is to improve the feature representation of brain MRI scans for more accurate classification. The proposed model is a hybrid MCI classification system that integrates three components: the Swin Transformer, the Dimension Centric Proximity Aware Attention Network (DCPAN), and the Age Deviation Factor (ADF). The proposed network achieves better classification results through a unique feature fusion approach that combines global, local, proximal features, and dimensional dependencies. It effectively combines fine-grained details with broader contextual information to extract discriminative features. Experimental results demonstrate the effectiveness of the proposed network, achieving an accuracy of 79.8%, precision of 76.6%, recall of 80.2%, and an F1-score of 78.4% when evaluated on the ADNI dataset.

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Section: Related Workmentioning

confidence: 99%

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

Illakiya,

Karthik

2023

IEEE Access

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“…Such a problem seriously impacts RNN performance. To alleviate this issue, some scholars proposed using Long Short-Term Memory (LSTM) networks [32] and gated current units (GRU) [33] to mitigate the impact of gradient disappearance.…”

Section: The Principles Of Long Short-term Memory Networkmentioning

confidence: 99%

A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target

Duan

et al. 2023

Sustainability

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The production and operation of corporates have a significant impact on the environment, and it is crucial for corporates to operate in an environmentally friendly manner, especially in the context of the China double carbon target. Corporate environmental performance refers to the degree of impact on the environment and the degree of contribution to environmental protection by corporates in their business activities. Our study conducted an assessment and early warning system for corporate environmental performance by monitoring seven typical corporate environmental performance variables, including the green asset ratio (Gra), the proportion of environmentally friendly products (Pefp), and cash flow for environmental protection to total assets ratio (ECF), of 2718 non-financial listed corporates in China’s A-share market. The dataset comprised empirical data from the CSMAR database and multi-scale measurements collected by us. Among data-driven monitoring methods, deep learning is widely applied due to its powerful automatic feature extraction abilities. However, multi-time scale data is often encountered in industrial ecology-related data, as the different underlying physical quantities of various data result in inconsistent sampling rates. Multi-time scale data are incomplete and asymmetrical, making it difficult for traditional models to use directly for corporate ecological monitoring. In this article, an improved CNN-LSTM monitoring model based on data fusion is proposed to address this issue. This method employs unified vectorization processing to transform incomplete multi-time scale data into uniform complete data. An end-to-end diagnostic model is constructed to simultaneously optimize feature extraction and monitoring. In a multi-time scale corporate monitoring model, CNN can mine hidden features of data, while LSTM can further capture the time dependence of underlying time series. Compared to manual feature extraction that relies on prior knowledge, the proposed model can learn more effective data features. The effectiveness of the method has been demonstrated through empirical data experiments, which is beneficial for corporates in the context of double carbon emissions, providing a method for regulating corporate ecological indicators.

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“…Sun et al [ 24 ] developed a hybrid model based on the LSTM network and CNN for predicting MCI and diagnosing AD. After collecting the fMRI images, the training samples were increased by implementing an adversarial network.…”

Section: Literature Reviewmentioning

confidence: 99%

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Chelladurai,

Narayan,

Divakarachari

et al. 2023

Brain Sciences

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In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

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Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Cited by 14 publications

References 40 publications

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

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