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Ahmad I, Siddiqi MH, Alhujaili SF, Alrowaili ZA. Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA. Healthcare (Basel) 2023; 11:2551. [PMID: 37761748 PMCID: PMC10530944 DOI: 10.3390/healthcare11182551] [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: 08/19/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.
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Affiliation(s)
- Irshad Ahmad
- Department of Computer Science, Islamia College, Peshawar 25000, KPK, Pakistan
| | - Muhammad Hameed Siddiqi
- College of Computer and Information Sciences, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
| | | | - Ziyad Awadh Alrowaili
- Department of Physics, College of Science, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
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Wang Y, Gu X, Hou W, Zhao M, Sun L, Guo C. Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data. Brain Sci 2023; 13:brainsci13020306. [PMID: 36831850 PMCID: PMC9954645 DOI: 10.3390/brainsci13020306] [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: 12/04/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Deep learning has shown impressive diagnostic abilities in Alzheimer's disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Xuming Gu
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Wenju Hou
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Meng Zhao
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun 130021, China
| | - Li Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun 130021, China
| | - Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Correspondence: ; Tel.: +86-1580-430-0151
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Wang X, Xin J, Wang Z, Chen Q, Wang Z. An evolving graph convolutional network for dynamic functional brain network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04203-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zhu J, Tan Y, Lin R, Miao J, Fan X, Zhu Y, Liang P, Gong J, He H. Efficient self-attention mechanism and structural distilling model for Alzheimer’s disease diagnosis. Comput Biol Med 2022; 147:105737. [DOI: 10.1016/j.compbiomed.2022.105737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/23/2022] [Accepted: 06/11/2022] [Indexed: 11/27/2022]
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Liu L, Chang J, Wang Y, Liang G, Wang YP, Zhang H. Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders. Front Neurosci 2022; 16:832276. [PMID: 35692429 PMCID: PMC9174798 DOI: 10.3389/fnins.2022.832276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.
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Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Jing Chang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Ying Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Gongbo Liang
- Department of Computer Science, Eastern Kentucky University, Richmond, KY, United States
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Hui Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
- *Correspondence: Hui Zhang
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Radiomic Features Based Severity Prediction in Dementia MR Images Using Hybrid SSA-PSO Optimizer and Multi-class SVM Classifier. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis. MATHEMATICS 2022. [DOI: 10.3390/math10081259] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of the brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner to implement preventative treatments. The segmentation of brain anatomy and categorization of AD have received increased attention since they can deliver good findings spanning a vast range of information. The first research gap considered in this work is the real-time efficiency of OTSU segmentation, which is not high, despite its simplicity and good accuracy. A second issue is that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for OTSU segmentation, and named EFEHO-OTSU. The main contribution of this work is twofold. One is utilizing EFEHO in the recommended technique to seek the optimal segmentation threshold for the OTSU method. Second, dual attention multi-instance deep learning network (DA-MIDL) is recommended for the timely diagnosis of AD and its prodromal phase, mild cognitive impairment (MCI). Tests show that this technique converges faster and takes less time than the classic OTSU approach without reducing segmentation performance. This study develops a valuable tool for quick picture segmentation with good real-time efficiency. Compared to numerous conventional techniques, the suggested study attains improved categorization performance regarding accuracy and transferability.
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Jiang M, Yan B, Li Y, Zhang J, Li T, Ke W. Image Classification of Alzheimer's Disease Based on External-Attention Mechanism and Fully Convolutional Network. Brain Sci 2022; 12:319. [PMID: 35326275 PMCID: PMC8946519 DOI: 10.3390/brainsci12030319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/11/2022] [Accepted: 02/16/2022] [Indexed: 01/27/2023] Open
Abstract
Automatic and accurate classification of Alzheimer's disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer's disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer's disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods.
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Affiliation(s)
- Mingfeng Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; (M.J.); (B.Y.)
| | - Bin Yan
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; (M.J.); (B.Y.)
| | - Yang Li
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; (M.J.); (B.Y.)
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310019, China;
| | - Tieqiang Li
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden;
| | - Wei Ke
- School of Applied Sciences, Macao Polytechnic Institute, Macao SAR 999078, China;
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Zhu Q, Wang Y, Zhuo C, Xu Q, Yao Y, Liu Z, Li Y, Sun Z, Wang J, Lv M, Wu Q, Wang D. Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning. Front Aging Neurosci 2022; 14:754334. [PMID: 35273489 PMCID: PMC8902140 DOI: 10.3389/fnagi.2022.754334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/12/2022] [Indexed: 01/29/2023] Open
Abstract
Objective Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive deterioration of memory and cognition. Mild cognitive impairment (MCI) has been implicated as a prodromal phase of AD. Although abnormal functional connectivity (FC) has been demonstrated in AD and MCI, the clinical differentiation of AD, MCI, and normal aging remains difficult, and the distinction between MCI and normal aging is especially problematic. We hypothesized that FC between the hippocampus and other brain structures is altered in AD and MCI, and that measurement of abnormal FC could have diagnostic utility for the classification of different AD stages. Methods Elderly adults aged 60–85 years were assigned to AD, MCI, or normal control (NC) groups based on clinical criteria. Functional magnetic resonance scanning was completed by 119 subjects. Five dimension reduction/classification methods were applied, using hippocampus-derived FC strengths as input features. Classification performance of the five dimensionality reduction methods was compared between AD, MCI, and NC groups. Results FCs between the hippocampus and left insula, left thalamus, cerebellum, right lingual gyrus, posterior cingulate cortex, and precuneus were significantly reduced in AD and MCI. Support vector machine learning coupled with sparse principal component analysis demonstrated the best discriminative performance, yielding classification accuracies of 82.02% (AD vs. NC), 81.33% (MCI vs. NC), and 81.08% (AD vs. MCI). Conclusion Hippocampus-seed-based FCs were significantly different between AD, MCI, and NC groups. FC assessment combined with widely used machine learning methods can improve AD differential diagnosis, and may be especially useful to distinguish MCI from normal aging.
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Affiliation(s)
- Qixiao Zhu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Yonghui Wang
- Department of Physical Medicine and Rehabilitation, Qilu Hospital of Shandong University, Jinan, China
| | - Chuanjun Zhuo
- Key Laboratory of Real Time Brain Circuits Tracing (RTBNP_Lab), Tianjin Fourth Center Hospital, Tianjin Fourth Hospital Affiliated to Nankai University, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Qunxing Xu
- Department of Health Management Center, Qilu Hospital of Shandong University, Jinan, China
| | - Yuan Yao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Zhuyun Liu
- Department of Radiology, The Second People’s Hospital of Rizhao City, Rizhao, China
| | - Yi Li
- Department of Neurology, Qilu Hospital of Shangdong University, Jinan, China
| | - Zhao Sun
- Shandong Chenze AI Research Institute Co. Ltd., Jinan, China
| | - Jian Wang
- Shandong Key Laboratory of Brain Function Remodeling, Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
| | - Ming Lv
- Department of Clinical Epidemiology, Qilu Hospital of Shandong University, Jinan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
| | - Qiang Wu
- School of Information Science and Engineering, Shandong University, Qingdao, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
- *Correspondence: Qiang Wu,
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
- Dawei Wang,
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Sharma R, Goel T, Tanveer M, Murugan R. FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer’s disease using the sagittal plane of MRI scans. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Li Y, Chen M. Near-infrared Two-region AIE Nanoprobe Study for AD Diagnosis and Treatment Integration. BIO WEB OF CONFERENCES 2022. [DOI: 10.1051/bioconf/20225501022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In this paper, we address the lack of specific targeting of β-amyloid β-protein (Aβ) in the current Alzheimer's disease (AD) diagnosis and treatment, and build a novel AIE nanoprobe molecule based on molecular design and nano-self-assembly technology. Based on the molecular design and nano-self-assembly technology, a new two-component AIE nanoprobe molecule with integrated AD diagnosis and treatment was constructed by co-assembling two-component AIE components. The NIR AIE nanomolecule can effectively penetrate the blood brain barrier (BBB) and depolymerize Aβ fibers, alleviate reactive oxygen species (ROS) in the focal area, and achieve highly sensitive imaging and specific depolymerization.
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Abstract
In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such as hippocampus shape analysis, fusion of embedded features, and so on, where EEG dataset used for AD diagnosis is usually is large and complex, requiring extraction of a series of features like entropy features, spectral feature, etc., and it has seldom been applied in the AD detection based on deep learning (DL), while MRI images were suitable for both ML and DL. In terms of the structural MRI brain images, few differences could be found in brain atrophy among the three situations: AD, mild cognitive impairment (MCI), and Normal Control (NC). On the other hand, DL methods have been used to diagnose AD incorporating MRI images in recent years, but there have not yet been many selective models with very deep layers. In this article, the Gray Matter (GM) Magnetic Resonance Imaging (MRI) is automatically extracted, which could better distinguish among the three types of situations like AD, MCI, and NC, compared with Cerebro Spinal Fluid (CSF) and White Matter (WM). Firstly, FMRIB Software Library (FSL) software is utilized for batch processing to remove the skull, cerebellum and register the heterogeneous images, and the SPM + cat12 tool kits in MATLAB is used to segment MRI images for obtaining the standard GM MRI images. Next, the GM MRI images are trained by some new neural networks. The characteristics of the training process are as follows: (1) The Tresnet, as the network that achieves the best classification effect among several new networks in the experiment, is selected as the basic network. (2) A multi-receptive-field mechanism is integrated into the network, which is inspired by neurons that can dynamically adjust the receptive fields according to different stimuli. (3) The whole network is realized by adding multiple channels to the convolutional layer, and the size of the convolution kernel of each channel can be dynamically adjusted. (4) Transfer learning method is used to train the model for speeding up the learning and optimizing the learning efficiency. Finally, we achieve the accuracies of 86.9% for AD vs. NC, 63.2% for AD vs. MCI vs. NC respectively, which outperform the previous approaches. The results demonstrate the effectiveness of our approach.
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Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning. APPLIED SYSTEM INNOVATION 2021. [DOI: 10.3390/asi4030049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.
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Chen X, Tao X, Wang FL, Xie H. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05588-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Ma X, Zhou Q. Special issue on deep learning and neural computing for intelligent sensing and control. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04785-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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