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Song Z, Li H, Zhang Y, Zhu C, Jiang M, Song L, Wang Y, Ouyang M, Hu F, Zheng Q. s 2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI. MAGMA (NEW YORK, N.Y.) 2024; 37:845-857. [PMID: 38869733 DOI: 10.1007/s10334-024-01178-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.
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Affiliation(s)
- Zhiwei Song
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Honglun Li
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College, Yantai, 264099, China
| | - Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000, China
| | - Yi Wang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
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Wang T, Ding Z, Yang X, Chen Y, Liu Y, Kong X, Sun Y. Detection of mild cognitive impairment based on attention mechanism and parallel dilated convolution. PeerJ Comput Sci 2024; 10:e2056. [PMID: 38855222 PMCID: PMC11157520 DOI: 10.7717/peerj-cs.2056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/22/2024] [Indexed: 06/11/2024]
Abstract
Mild cognitive impairment (MCI) is a precursor to neurodegenerative diseases such as Alzheimer's disease, and an early diagnosis and intervention can delay its progression. However, the brain MRI images of MCI patients have small changes and blurry shapes. At the same time, MRI contains a large amount of redundant information, which leads to the poor performance of current MCI detection methods based on deep learning. This article proposes an MCI detection method that integrates the attention mechanism and parallel dilated convolution. By introducing an attention mechanism, it highlights the relevant information of the lesion area in the image, suppresses irrelevant areas, eliminates redundant information in MRI images, and improves the ability to mine detailed information. Parallel dilated convolution is used to obtain a larger receptive field without downsampling, thereby enhancing the ability to acquire contextual information and improving the accuracy of small target classification while maintaining detailed information on large-scale feature maps. Experimental results on the public dataset ADNI show that the detection accuracy of the method on MCI reaches 81.63%, which is approximately 6.8% higher than the basic model. The method is expected to be used in clinical practice in the future to provide earlier intervention and treatment for MCI patients, thereby improving their quality of life.
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Affiliation(s)
- Tao Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Zenghui Ding
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Xianjun Yang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Yanyan Chen
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Yu Liu
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, Anhui, China
- Hefei Fourth People’s Hospital, Hefei, Anhui, China
| | - Xiaoming Kong
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, Anhui, China
- Hefei Fourth People’s Hospital, Hefei, Anhui, China
| | - Yining Sun
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
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Awarayi NS, Twum F, Hayfron-Acquah JB, Owusu-Agyemang K. A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN. PLoS One 2024; 19:e0302358. [PMID: 38640105 PMCID: PMC11029622 DOI: 10.1371/journal.pone.0302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 04/03/2024] [Indexed: 04/21/2024] Open
Abstract
This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.
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Affiliation(s)
- Nicodemus Songose Awarayi
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana
| | - Frimpong Twum
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - James Ben Hayfron-Acquah
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Kwabena Owusu-Agyemang
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Tang Y, Xiong X, Tong G, Yang Y, Zhang H. Multimodal diagnosis model of Alzheimer's disease based on improved Transformer. Biomed Eng Online 2024; 23:8. [PMID: 38243275 PMCID: PMC10799436 DOI: 10.1186/s12938-024-01204-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
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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Affiliation(s)
- Yan Tang
- School of Electronic Information, Central South University, Changsha, 410008, Hunan, People's Republic of China
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China
| | - Xing Xiong
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Gan Tong
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Yuan Yang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Grainger College of Engineering, Urbana, IL, USA
| | - Hao Zhang
- School of Electronic Information, Central South University, Changsha, 410008, Hunan, People's Republic of China.
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Fathi S, Ahmadi A, Dehnad A, Almasi-Dooghaee M, Sadegh M. A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images. Neuroinformatics 2024; 22:89-105. [PMID: 38042764 PMCID: PMC10917836 DOI: 10.1007/s12021-023-09646-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 12/04/2023]
Abstract
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.
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Affiliation(s)
- Sina Fathi
- Department of Health Information Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Ahmadi
- Surrey Business School, University of Surrey, Guildford Surrey, GU2 7XH, UK.
| | - Afsaneh Dehnad
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Almasi-Dooghaee
- Neurology Department, Firoozgar Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Melika Sadegh
- Neurology Department, Firoozgar Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Almufareh MF, Tehsin S, Humayun M, Kausar S. Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer's Disease. Healthcare (Basel) 2023; 11:2763. [PMID: 37893836 PMCID: PMC10606602 DOI: 10.3390/healthcare11202763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
Alzheimer's disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer's in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer's disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer's using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset.
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Affiliation(s)
- Maram Fahaad Almufareh
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi Arabia;
| | - Samabia Tehsin
- Department of Computer Science, Bahria University, Islamabad 44000, Pakistan; (S.T.); (S.K.)
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi Arabia;
| | - Sumaira Kausar
- Department of Computer Science, Bahria University, Islamabad 44000, Pakistan; (S.T.); (S.K.)
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Cao G, Zhang M, Wang Y, Zhang J, Han Y, Xu X, Huang J, Kang G. End-to-end automatic pathology localization for Alzheimer's disease diagnosis using structural MRI. Comput Biol Med 2023; 163:107110. [PMID: 37321102 DOI: 10.1016/j.compbiomed.2023.107110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is an essential part of the clinical assessment of patients at risk of Alzheimer dementia. One key challenge in sMRI-based computer-aided dementia diagnosis is to localize local pathological regions for discriminative feature learning. Existing solutions predominantly depend on generating saliency maps for pathology localization and handle the localization task independently of the dementia diagnosis task, leading to a complex multi-stage training pipeline that is hard to optimize with weakly-supervised sMRI-level annotations. In this work, we aim to simplify the pathology localization task and construct an end-to-end automatic localization framework (AutoLoc) for Alzheimer's disease diagnosis. To this end, we first present an efficient pathology localization paradigm that directly predicts the coordinate of the most disease-related region in each sMRI slice. Then, we approximate the non-differentiable patch-cropping operation with the bilinear interpolation technique, which eliminates the barrier to gradient backpropagation and thus enables the joint optimization of localization and diagnosis tasks. Extensive experiments on commonly used ADNI and AIBL datasets demonstrate the superiority of our method. Especially, we achieve 93.38% and 81.12% accuracy on Alzheimer's disease classification and mild cognitive impairment conversion prediction tasks, respectively. Several important brain regions, such as rostral hippocampus and globus pallidus, are identified to be highly associated with Alzheimer's disease.
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Affiliation(s)
- Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Manli Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Yiping Wang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Jing Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
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Liu Y, Mazumdar S, Bath PA. An unsupervised learning approach to diagnosing Alzheimer's disease using brain magnetic resonance imaging scans. Int J Med Inform 2023; 173:105027. [PMID: 36921480 DOI: 10.1016/j.ijmedinf.2023.105027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. Due to the lack of effectiveness of manual diagnosis by doctors, machine learning is now being applied to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. OBJECTIVE This study applied a statistical method and unsupervised learning methods to discriminate between scans from cognitively normal (CN) and people with AD using a limited number of labelled structural MRI scans. METHODS We used two-sample t-tests to detect the AD-relevant regions, and then employed an unsupervised learning neural network to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on baseline brain structural MRI scans from 429 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of which 231 were CN and 198 had AD. RESULTS The abnormal regions around the lower parts of limbic system were indicated as AD-relevant regions based on the two-sample t-test (p < 0.001), and the proposed method yielded an accuracy of 0.84 for discriminating between CN and AD. CONCLUSION The study combined statistical and unsupervised learning methods to identify scans of people with AD. This method can detect AD-relevant regions and could be used to accurately diagnose AD; it does not require large amounts of labelled MRI scans. Our research could help in the automatic diagnosis of AD and provide a basis for diagnosing stable mild cognitive impairment (stable MCI) and progressive mild cognitive impairment (progressive MCI).
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Affiliation(s)
- Yuyang Liu
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK.
| | - Suvodeep Mazumdar
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
| | - Peter A Bath
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
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- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
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Li H, Tan Y, Miao J, Liang P, Gong J, He H, Jiao Y, Zhang F, Xing Y, Wu D. Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer’s disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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10
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Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X. MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI. Comput Biol Med 2023; 157:106790. [PMID: 36958239 DOI: 10.1016/j.compbiomed.2023.106790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/13/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
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Affiliation(s)
- Fei Liu
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
| | - Shiuan-Ni Liang
- School of Engineering, Monash University Malaysia, Kuala Lumpur, Malaysia
| | - Zhe Jin
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Shicheng Wei
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Xuejun Li
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
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Dai WZ, Liu L, Zhu MZ, Lu J, Ni JM, Li R, Ma T, Zhu XC. Morphological and Structural Network Analysis of Sporadic Alzheimer's Disease Brains Based on the APOE4 Gene. J Alzheimers Dis 2023; 91:1035-1048. [PMID: 36530087 DOI: 10.3233/jad-220877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is an increasingly common type of dementia. Apolipoprotein E (APOE) gene is a strong risk factor for AD. OBJECTIVE Here, we explored alterations in grey matter structure (GMV) and networks in AD, as well as the effects of the APOEɛ4 allele on neuroimaging regions based on structural magnetic resonance imaging (sMRI). METHODS All subjects underwent an sMRI scan. GMV and cortical thickness were calculated using voxel-based morphological analysis, and structural networks were constructed based on graph theory analysis to compare differences between AD and normal controls. RESULTS The volumes of grey matter in the bilateral inferior temporal gyrus, right middle temporal gyrus, right inferior parietal lobule, right limbic lobe, right frontal lobe, left anterior cingulate gyrus, and bilateral olfactory cortex of patients with AD were significantly decreased. The cortical thickness in patients with AD was significantly reduced in the left lateral occipital lobe, inferior parietal lobe, orbitofrontal region, precuneus, superior parietal gyrus, right precentral gyrus, middle temporal gyrus, pars opercularis gyrus, insular gyrus, superior marginal gyrus, bilateral fusiform gyrus, and superior frontal gyrus. In terms of local properties, there were significant differences between the AD and control groups in these areas, including the right bank, right temporalis pole, bilateral middle temporal gyrus, right transverse temporal gyrus, left postcentral gyrus, and left parahippocampal gyrus. CONCLUSION There were significant differences in the morphological and structural covariate networks between AD patients and healthy controls under APOEɛ4 allele effects.
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Affiliation(s)
- Wen-Zhuo Dai
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Lu Liu
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Meng-Zhuo Zhu
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China
| | - Jing Lu
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Jian-Ming Ni
- Radiology Department, Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Rong Li
- Department of Pharmacy, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
| | - Tao Ma
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
| | - Xi-Chen Zhu
- Department of Neurology, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.,Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China.,Department of Neurology, Affiliated Wuxi No. 2 Hospital of Jiangnan University, Wuxi, Jiangsu Province, China
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Devika K, Mahapatra D, Subramanian R, Ramana Murthy Oruganti V. Dense Attentive GAN-based One-Class Model for Detection of Autism and ADHD. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Khojaste-Sarakhsi M, Haghighi SS, Ghomi SF, Marchiori E. Deep learning for Alzheimer's disease diagnosis: A survey. Artif Intell Med 2022; 130:102332. [DOI: 10.1016/j.artmed.2022.102332] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/29/2022] [Accepted: 05/30/2022] [Indexed: 11/28/2022]
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Fathi S, Ahmadi M, Dehnad A. Early diagnosis of Alzheimer's disease based on deep learning: A systematic review. Comput Biol Med 2022; 146:105634. [DOI: 10.1016/j.compbiomed.2022.105634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 04/25/2022] [Accepted: 04/25/2022] [Indexed: 11/03/2022]
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Baghdadi NA, Malki A, Balaha HM, Badawy M, Elhosseini M. A 3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22114250. [PMID: 35684871 PMCID: PMC9185328 DOI: 10.3390/s22114250] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/24/2022] [Accepted: 05/28/2022] [Indexed: 05/10/2023]
Abstract
Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.M.); (M.E.)
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
- Correspondence:
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.M.); (M.E.)
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
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Ashtari-Majlan M, Seifi A, Dehshibi MM. A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images. IEEE J Biomed Health Inform 2022; 26:3918-3926. [PMID: 35239494 DOI: 10.1109/jbhi.2022.3155705] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Early diagnosis of Alzheimers 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 Alzheimers 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 Alzheimers 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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