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Shoaip N, El-Sappagh S, Abuhmed T, Elmogy M. A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning. Sci Rep 2024; 14:4275. [PMID: 38383597 PMCID: PMC10881567 DOI: 10.1038/s41598-024-54065-1] [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: 06/30/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.
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Affiliation(s)
- Nora Shoaip
- Information Systems Department, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt
- Department of Computer Science and Engineering, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Tamer Abuhmed
- Department of Computer Science and Engineering, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
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Li J, Yang M, Wei R, Cao Y, Fan X, Zhang S. The Predictive Ability of Blood Neurofilament Light Chain in Predicting Cognitive Decline in the Alzheimer's Disease Continuum: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2024; 97:1589-1620. [PMID: 38306045 DOI: 10.3233/jad-231080] [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] [Indexed: 02/03/2024]
Abstract
Background Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset. Identifying candidate predictors to forecast AD dementia risk before disease onset is crucial for early diagnosis and treatment. Objective We aimed to assess the predictive ability of blood neurofilament light (NfL) chain in anticipating cognitive decline in the AD continuum. Methods We systematically searched PubMed, Web of Science, and Embase from inception until April 7, 2023. Longitudinal observational studies examining the association between baseline blood NfL and cognitive decline or clinical disease conversion were included based on inclusion/exclusion criteria. The final effect size was represented by adjusted hazard ratios (HR) or standardized beta (s.β) coefficients with a 95% confidence interval (CI). Results A total of 2,862 articles were identified, and 26 studies were included in this meta-analysis. The results indicated that baseline blood NfL could predict cognitive decline, with MMSE [s.β= -0.17, 95% CI (-0.26, -0.07)]; PACC [s.β= -0.09, 95% CI (-0.16, -0.03)]; ADAS-cog [s.β= 0.21, 95% CI (0.13, 0.29)]; CDR-SOB [s.β= 0.27, 95% CI (0.03, 0.50)]; Global cognitive composite [s.β= -0.05, 95% CI (-0.08, -0.01)]; Memory subdomain [s.β= -0.06, 95% CI (-0.09, -0.03)]; Language subdomain [s.β= -0.07, 95% CI (-0.10, -0.05)]; Executive function subdomain [s.β= -0.02, 95% CI (-0.03, -0.01)]; Visuospatial subdomain [s.β= -0.06, 95% CI (-0.08, -0.04)]. Additionally, baseline blood NfL could predict disease progression (conversion from CU/SCD/MCI to MCI/AD) in the AD continuum [Adjust HR = 1.32, 95% CI (1.12, 1.56)]. Conclusions Baseline blood NfL demonstrated predictive capabilities for global cognition and its memory, language, executive function, visuospatial subdomains decline in the AD continuum. Moreover, it exhibited the potential to predict disease progression in non-AD dementia participants.
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Affiliation(s)
- Jianhong Li
- Fujian Key Laboratory of Aptamers Technology, 900TH hospital of Joint Logistics Support Force, People's Liberation Army (PLA), Fuzhou, Fujian, China
| | - Minguang Yang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Renli Wei
- The Institute of Rehabilitation Industry, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Yue Cao
- Fujian Key Laboratory of Aptamers Technology, 900TH hospital of Joint Logistics Support Force, People's Liberation Army (PLA), Fuzhou, Fujian, China
| | - Xu Fan
- The Institute of Rehabilitation Industry, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Shenghang Zhang
- Fujian Key Laboratory of Aptamers Technology, 900TH hospital of Joint Logistics Support Force, People's Liberation Army (PLA), Fuzhou, Fujian, China
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Kishore N, Goel N. Deep learning based diagnosis of Alzheimer's disease using FDG-PET images. Neurosci Lett 2023; 817:137530. [PMID: 37858874 DOI: 10.1016/j.neulet.2023.137530] [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: 09/07/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnostic tool that accurately detects glucose metabolism in the brain of AD patients. MATERIAL AND METHODS In this work, we have developed a deep neural network using FDG-PET to discriminate Alzheimer's disease subjects from stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (pMCI), and cognitively normal (CN) cohorts. A total of 83 FDG-PET scans are collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 21 subjects with CN, 21 subjects with sMCI, 21 subjects with pMCI, and 20 subjects with AD. RESULTS The method has achieved remarkable accuracy rates of 99.31% for CN vs. AD, 99.88% for CN vs. MCI, 99.54% for AD vs. MCI, and 96.81% for pMCI vs. sMCI. Based on the experimental results. CONCLUSION The results show that the proposed method has a significant generalisation ability as well as good performance in predicting the conversion of MCI to AD even in the absence of direct information. FDG-PET is a well-known biomarker for the identification of Alzheimer's disease using transfer learning.
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Affiliation(s)
- Nand Kishore
- Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India
| | - Neelam Goel
- Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India.
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Odusami M, Maskeliūnas R, Damaševičius R. Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer's Disease Diagnosis: Enhancing Data Integration and Feature Extraction. J Pers Med 2023; 13:1496. [PMID: 37888107 PMCID: PMC10608760 DOI: 10.3390/jpm13101496] [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: 09/16/2023] [Revised: 09/28/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Multimodal neuroimaging has gained traction in Alzheimer's Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of instance normalization, both of which play crucial roles in feature extraction from magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Specifically, varying kernel sizes allow us to adapt the receptive field to different image characteristics, enhancing the model's ability to capture relevant information. Furthermore, we employ transposed convolution, which increases spatial resolution of feature maps, and it is optimized with varying kernel sizes and instance normalization. This heightened resolution facilitates the alignment and integration of data from disparate MRI and PET data. The use of larger kernels and strides in transposed convolution expands the receptive field, enabling the model to capture essential cross-modal relationships. Instance normalization, applied to each modality during the fusion process, mitigates potential biases stemming from differences in intensity, contrast, or scale between modalities. This enhancement contributes to improved model performance by reducing complexity and ensuring robust fusion. The performance of the proposed fusion method is assessed on three distinct neuroimaging datasets, which include: Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD, and Early Mild Cognitive); Open Access Series of Imaging Studies (OASIS), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, Mild Dementia, Very Mild Dementia); and whole-brain atlas neuroimaging (AANLIB) (consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD). To evaluate the quality of the fused images generated via our method, we employ a comprehensive set of evaluation metrics, including Structural Similarity Index Measurement (SSIM), which assesses the structural similarity between two images; Peak Signal-to-Noise Ratio (PSNR), which measures how closely the generated image resembles the ground truth; Entropy (E), which assesses the amount of information preserved or lost during fusion; the Feature Similarity Indexing Method (FSIM), which assesses the structural and feature similarities between two images; and Edge-Based Similarity (EBS), which measures the similarity of edges between the fused and ground truth images. The obtained fused image is further evaluated using a Mobile Vision Transformer. In the classification of AD vs. Cognitive Normal, the model achieved an accuracy of 99.00%, specificity of 99.00%, and sensitivity of 98.44% on the AANLIB dataset.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 53361 Kaunas, Lithuania
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Ozsari S, Güzel MS, Yılmaz D, Kamburoğlu K. A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases. Diagnostics (Basel) 2023; 13:2700. [PMID: 37627959 PMCID: PMC10453523 DOI: 10.3390/diagnostics13162700] [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: 07/11/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.
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Affiliation(s)
- Sifa Ozsari
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Mehmet Serdar Güzel
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Dilek Yılmaz
- Faculty of Dentistry, Baskent University, 06490 Ankara, Turkey;
| | - Kıvanç Kamburoğlu
- Department of Dentomaxillofacial Radiology, Ankara University, 06560 Ankara, Turkey;
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Agarwal D, Berbís MÁ, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47:57. [PMID: 37129723 PMCID: PMC10154284 DOI: 10.1007/s10916-023-01941-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/20/2023] [Indexed: 05/03/2023]
Abstract
Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.
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Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain.
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Médica, Carmelo Torres No. 2, 23007, Jaén, Spain
| | - Vivian Lipari
- European Atlantic University, Isabel Torres 21, 39011, Santander, Spain
| | | | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain
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7
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Zhao Z, Chuah JH, Lai KW, Chow CO, Gochoo M, Dhanalakshmi S, Wang N, Bao W, Wu X. Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review. Front Comput Neurosci 2023; 17:1038636. [PMID: 36814932 PMCID: PMC9939698 DOI: 10.3389/fncom.2023.1038636] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.
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Affiliation(s)
- Zhen Zhao
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,*Correspondence: Joon Huang Chuah ✉
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Khin Wee Lai ✉
| | - Chee-Onn Chow
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Munkhjargal Gochoo
- Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Na Wang
- School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Wei Bao
- China Electronics Standardization Institute, Beijing, China,Wei Bao ✉
| | - Xiang Wu
- School of Medical Information Engineering, Xuzhou Medical University, Xuzhou, China
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8
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Recent Advances in Cardiovascular Diseases Research Using Animal Models and PET Radioisotope Tracers. Int J Mol Sci 2022; 24:ijms24010353. [PMID: 36613797 PMCID: PMC9820417 DOI: 10.3390/ijms24010353] [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: 11/10/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVD) is a collective term describing a range of conditions that affect the heart and blood vessels. Due to the varied nature of the disorders, distinguishing between their causes and monitoring their progress is crucial for finding an effective treatment. Molecular imaging enables non-invasive visualisation and quantification of biological pathways, even at the molecular and subcellular levels, what is essential for understanding the causes and development of CVD. Positron emission tomography imaging is so far recognized as the best method for in vivo studies of the CVD related phenomena. The imaging is based on the use of radioisotope-labelled markers, which have been successfully used in both pre-clinical research and clinical studies. Current research on CVD with the use of such radioconjugates constantly increases our knowledge and understanding of the causes, and brings us closer to effective monitoring and treatment. This review outlines recent advances in the use of the so-far available radioisotope markers in the research on cardiovascular diseases in rodent models, points out the problems and provides a perspective for future applications of PET imaging in CVD studies.
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Alzheimer’s Disease Prediction Algorithm Based on Group Convolution and a Joint Loss Function. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1854718. [PMID: 36277022 PMCID: PMC9581650 DOI: 10.1155/2022/1854718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) can effectively predict by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) of the brain, but current PET images still suffer from indistinct lesion features, low signal-to-noise ratios, and severe artefacts, resulting in poor prediction accuracy for patients with mild cognitive impairment (MCI) and unclear lesion features. In this paper, an AD prediction algorithm based on group convolution and a joint loss function is proposed. First, a group convolutional backbone network based on ResNet18 is designed to extract lesion features from multiple channels, which makes the expression ability of the network improved to a great extent. Then, a hybrid attention mechanism is presented, which enables the network to focus on target regions and learn feature weights, so as to enhance the network's learning ability of the lesion regions that are relevant to disease diagnosis. Finally, a joint loss function, that avoids the overfitting phenomenon, increases the generalization of the model, and improves prediction accuracy by adding a regularization loss function to the conventional cross-entropy function, is proposed. Experiments conducted on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the algorithm we proposed gives a prediction accuracy improvement of 2.4% over that of the current AD prediction algorithm, thus proving the effectiveness and availability of the new algorithm.
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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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End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer’s Diagnosis. MATHEMATICS 2022. [DOI: 10.3390/math10152575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings.
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Bi XA, Li L, Wang Z, Wang Y, Luo X, Xu L. IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data. Brief Bioinform 2022; 23:6554128. [PMID: 35348583 DOI: 10.1093/bib/bbac093] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Lou Li
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zizheng Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yu Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Luyun Xu
- College of Business, Hunan Normal University, Changsha 410081, P.R. China
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Park C, Gim J, Lee S, Lee KJ, Kim JS. Automated Synapse Detection Method for Cerebellar Connectomics. Front Neuroanat 2022; 16:760279. [PMID: 35360651 PMCID: PMC8963724 DOI: 10.3389/fnana.2022.760279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
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Affiliation(s)
- Changjoo Park
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jawon Gim
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungjin Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Kea Joo Lee
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
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An efficient combination of quadruple biomarkers in binary classification using ensemble machine learning technique for early onset of Alzheimer disease. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07076-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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15
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Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease. Metabolites 2022; 12:metabo12030231. [PMID: 35323674 PMCID: PMC8954205 DOI: 10.3390/metabo12030231] [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: 02/14/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.
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16
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Cui W, Yan C, Yan Z, Peng Y, Leng Y, Liu C, Chen S, Jiang X, Zheng J, Yang X. BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images. Front Neurosci 2022; 16:831533. [PMID: 35281501 PMCID: PMC8908419 DOI: 10.3389/fnins.2022.831533] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/11/2022] [Indexed: 12/21/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module. In addition, the accuracy of classification between EMCI and LMCI achieves 79.64% using imbalanced FDG-PET images, which illustrates that the proposed method yields a state-of-the-art result of the classification accuracy between EMCI and LMCI based on PET images.
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Affiliation(s)
- Wenju Cui
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Caiying Yan
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Yunsong Peng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yilin Leng
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Chenlu Liu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Shuangqing Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Xi Jiang
- School of Life Sciences and Technology, The University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Zheng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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17
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Sun X, Guo W, Shen J. Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data. Front Neurosci 2022; 16:1043626. [PMID: 36741058 PMCID: PMC9889549 DOI: 10.3389/fnins.2022.1043626] [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: 09/13/2022] [Accepted: 12/12/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Brain degeneration is commonly caused by some chronic diseases, such as Alzheimer's disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data). Methods Several deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results. Results As different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively. Discussion The prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods.
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Affiliation(s)
- Xiaofei Sun
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Weiwei Guo
- EchoX Technology Limited, Hong Kong, Hong Kong SAR, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
- *Correspondence: Jing Shen,
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Accuracy versus reliability-based modelling approaches for medical decision making. Comput Biol Med 2021; 141:105138. [PMID: 34929467 DOI: 10.1016/j.compbiomed.2021.105138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/21/2022]
Abstract
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
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Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F. Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7259. [PMID: 34770565 PMCID: PMC8587338 DOI: 10.3390/s21217259] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
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Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Gonçalo Marques
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
- Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Manuel A. Franco Martin
- Psychiatric Department, University Rio Hortega Hospital–Valladolid, 47011 Valladolid, Spain;
| | - Begoña García Zapiraín
- eVIDA Laboratory, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;
| | - Francisco Martín Rodríguez
- Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, 47011 Valladolid, Spain;
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20
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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21
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Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Maqsood M, Yaqub M. A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience 2021; 460:43-52. [PMID: 33465405 DOI: 10.1016/j.neuroscience.2021.01.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/23/2020] [Accepted: 01/03/2021] [Indexed: 01/07/2023]
Abstract
Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a crucial role in the treatment of dementia disease at an early stage. Deep learning architecture produces impressive results in such research. Algorithms require a large number of annotated datasets for training the model. In this study, we overcome this issue by using layer-wise transfer learning as well as tissue segmentation of brain images to diagnose the early stage of Alzheimer's disease (AD). In layer-wise transfer learning, we used the VGG architecture family with pre-trained weights. The proposed model segregates between normal control (NC), the early mild cognitive impairment (EMCI), the late mild cognitive impairment (LMCI), and the AD. In this paper, 85 NC patients, 70 EMCI, 70 LMCI, and 75 AD patients access form the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Tissue segmentation was applied on each subject to extract the gray matter (GM) tissue. In order to check the validity, the proposed method is tested on preprocessing data and achieved the highest rates of the classification accuracy on AD vs NC is 98.73%, also distinguish between EMCI vs LMCI patients testing accuracy 83.72%, whereas remaining classes accuracy is more than 80%. Finally, we provide a comparative analysis with other studies which shows that the proposed model outperformed the state-of-the-art models in terms of testing accuracy.
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Affiliation(s)
- Atif Mehmood
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Shuyuan Yang
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China.
| | - Zhixi Feng
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Min Wang
- Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
| | - Al Smadi Ahmad
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Rizwan Khan
- School of Electronic Information and Communications, HUST University, Wuhan 4370074, China
| | - Muazzam Maqsood
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Muhammad Yaqub
- Faculty of Information Technology, Beijing University of Technology, Beijing 10000, China
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22
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Shirbandi K, Khalafi M, Mirza-Aghazadeh-Attari M, Tahmasbi M, Kiani Shahvandi H, Javanmardi P, Rahim F. Accuracy of deep learning model-assisted amyloid positron emission tomography scan in predicting Alzheimer's disease: A Systematic Review and meta-analysis. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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