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Martinez-Murcia FJ, Arco JE, Jimenez-Mesa C, Segovia F, Illan IA, Ramirez J, Gorriz JM. Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning. Int J Neural Syst 2024; 34:2450043. [PMID: 38770651 DOI: 10.1142/s0129065724500436] [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: 05/22/2024]
Abstract
Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.
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Affiliation(s)
- Francisco J Martinez-Murcia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Eloy Arco
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Fermin Segovia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Ignacio A Illan
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
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2
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Malik I, Iqbal A, Gu YH, Al-antari MA. Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review. Diagnostics (Basel) 2024; 14:1281. [PMID: 38928696 PMCID: PMC11202897 DOI: 10.3390/diagnostics14121281] [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: 05/20/2024] [Revised: 06/10/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer's disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.
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Affiliation(s)
- Isra Malik
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 44000, Pakistan
| | - Ahmed Iqbal
- Department of Computer Science, Sir Syed Case Institute of Technology, Islamabad 45230, Pakistan
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
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3
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Madni HA, Umer RM, Foresti GL. Robust Federated Learning for Heterogeneous Model and Data. Int J Neural Syst 2024; 34:2450019. [PMID: 38414421 DOI: 10.1142/s0129065724500199] [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: 02/29/2024]
Abstract
Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.
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Affiliation(s)
- Hussain Ahmad Madni
- Department of Mathematics, Computer Science and Physics (DMIF), University of Udine, Udine 33100, Italy
| | - Rao Muhammad Umer
- Institute of AI for Health, Helmholtz Zentrum München - German Research, Center for Environmental Health, Neuherberg 85764, Germany
| | - Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics (DMIF), University of Udine, Udine 33100, Italy
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4
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Zanin M, Aktürk T, Yıldırım E, Yerlikaya D, Yener G, Güntekin B. Reconstructing brain functional networks through identifiability and deep learning. Netw Neurosci 2024; 8:241-259. [PMID: 38562295 PMCID: PMC10923503 DOI: 10.1162/netn_a_00353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain
| | - Tuba Aktürk
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
- School of Medicine, Izmir University of Economics, Izmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
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5
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Sheng J, Zhang Q, Zhang Q, Wang L, Yang Z, Xin Y, Wang B. A hybrid multimodal machine learning model for Detecting Alzheimer's disease. Comput Biol Med 2024; 170:108035. [PMID: 38325214 DOI: 10.1016/j.compbiomed.2024.108035] [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: 11/14/2023] [Revised: 01/03/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.
| | - Qian Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China; National Center of Gerontology, Beijing, 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Ze Yang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Binbing Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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6
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Adarsh V, Gangadharan GR, Fiore U, Zanetti P. Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis. Sci Rep 2024; 14:1774. [PMID: 38245656 PMCID: PMC10799876 DOI: 10.1038/s41598-024-52185-2] [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: 04/19/2023] [Accepted: 01/15/2024] [Indexed: 01/22/2024] Open
Abstract
The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.
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Affiliation(s)
- V Adarsh
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
| | - G R Gangadharan
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
| | - Ugo Fiore
- University of Salerno, Fisciano, Italy
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7
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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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8
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Shanmugavadivel K, Sathishkumar VE, Cho J, Subramanian M. Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection. Ageing Res Rev 2023; 91:102072. [PMID: 37709055 DOI: 10.1016/j.arr.2023.102072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/05/2023] [Accepted: 09/10/2023] [Indexed: 09/16/2023]
Abstract
Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually causes brain cells to die. This neurological condition progressively hampers cognitive and memory functions, along with the ability to carry out fundamental tasks over time. From the symptoms it is very difficult to detect during its early stage. It has become necessary to develop a computer assisted diagnostic models for the early AD detection. This survey work, discussed about a review of 110 published AD detection methods and techniques from the year 2011 to till-date. This study lies in its comprehensive exploration of AD detection methods using a range of artificial intelligence (AI) techniques and neuroimaging modalities. By collecting and analysing 50 papers related to AD diagnosis datasets, the study provides a comprehensive understanding of the diversity of input types, subjects, and classes used in AD research. Summarizing 60 papers on methodologies gives researchers a succinct overview of various approaches that contribute to enhancing detection accuracy. From the review, data are acquired and pre-processed form multiple modalities of neuroimaging. This paper mainly focused on review of different datasets used, various feature extraction methods, parameters used in neuro images. To diagnosis the Alzheimer's disease, the existing methods utilized three most common artificial intelligence techniques such as machine learning, deep learning, and transfer learning. We conclude this survey work by providing future perspectives for AD diagnosis at early stage.
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Affiliation(s)
| | - V E Sathishkumar
- Department of Software Engineering, Jeonbuk National University, Jeonju-si, Jeollabuk-do 54896, Republic of Korea
| | - Jaehyuk Cho
- Department of Software Engineering, Jeonbuk National University, Jeonju-si, Jeollabuk-do 54896, Republic of Korea.
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9
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Zhou Q, Wang S, Zhu H, Zhang X, Zhang Y. Multiple-instance ensemble for construction of deep heterogeneous committees for high-dimensional low-sample-size data. Neural Netw 2023; 167:380-399. [PMID: 37673026 DOI: 10.1016/j.neunet.2023.08.028] [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: 11/09/2022] [Revised: 07/22/2023] [Accepted: 08/17/2023] [Indexed: 09/08/2023]
Abstract
Deep ensemble learning, where we combine knowledge learned from multiple individual neural networks, has been widely adopted to improve the performance of neural networks in deep learning. This field can be encompassed by committee learning, which includes the construction of neural network cascades. This study focuses on the high-dimensional low-sample-size (HDLS) domain and introduces multiple instance ensemble (MIE) as a novel stacking method for ensembles and cascades. In this study, our proposed approach reformulates the ensemble learning process as a multiple-instance learning problem. We utilise the multiple-instance learning solution of pooling operations to associate feature representations of base neural networks into joint representations as a method of stacking. This study explores various attention mechanisms and proposes two novel committee learning strategies with MIE. In addition, we utilise the capability of MIE to generate pseudo-base neural networks to provide a proof-of-concept for a "growing" neural network cascade that is unbounded by the number of base neural networks. We have shown that our approach provides (1) a class of alternative ensemble methods that performs comparably with various stacking ensemble methods and (2) a novel method for the generation of high-performing "growing" cascades. The approach has also been verified across multiple HDLS datasets, achieving high performance for binary classification tasks in the low-sample size regime.
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Affiliation(s)
- Qinghua Zhou
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Hengde Zhu
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Xin Zhang
- Department of Medical Imaging, Huai'an Infectious Disease Hospital, Jiangsu, 223002, China.
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
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10
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Saleh H, Amer E, Abuhmed T, Ali A, Al-Fuqaha A, El-Sappagh S. Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data. Sci Rep 2023; 13:16336. [PMID: 37770490 PMCID: PMC10539296 DOI: 10.1038/s41598-023-42796-6] [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: 03/24/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient's multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient's status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient's multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer's Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Eslam Amer
- Communications and Information Technology, The Institute of Electronics, Queen's University of Belfast, Belfast, UK
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Seoul, Suwon, 16419, South Korea.
| | - Amjad Ali
- Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar
| | - Ala Al-Fuqaha
- Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Seoul, Suwon, 16419, South Korea.
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt.
- Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
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11
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Saleh H, Elrashidy N, Elaziz MA, Aseeri AO, El-sappagh S. Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression.
We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Nora ElRashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh, 13518, Egypt
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
| | - Ahmad O. Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
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12
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Zhang Y, Fan W, Chen X, Li W. The Objective Dementia Severity Scale Based on MRI with Contrastive Learning: A Whole Brain Neuroimaging Perspective. SENSORS (BASEL, SWITZERLAND) 2023; 23:6871. [PMID: 37571654 PMCID: PMC10422209 DOI: 10.3390/s23156871] [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: 06/29/2023] [Revised: 07/25/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023]
Abstract
In the clinical treatment of Alzheimer's disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. To address these issues, we propose an objective dementia severity scale based on MRI (ODSS-MRI) using contrastive learning to automatically evaluate the neurological function of patients. The approach utilizes a deep learning framework and a contrastive learning strategy to mine relevant information from structural magnetic resonance images to obtain the patient's neurological function level score. Given that the model is driven by the patient's whole brain imaging data, but without any possible biased manual intervention or instruction from the physician or patient, it provides a comprehensive and objective evaluation of the patient's neurological function. We conducted experiments on the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, and the results showed that the proposed ODSS-MRI was correlated with the stages of AD 88.55% better than all existing methods. This demonstrates its efficacy to describe the neurological function changes of patients during AD progression. It also outperformed traditional psychiatric rating scales in discriminating different stages of AD, which is indicative of its superiority for neurological function evaluation.
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Affiliation(s)
- Yike Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China, Wuhan 430074, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xi Chen
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China, Wuhan 430074, China
| | - Wei Li
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China, Wuhan 430074, China
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13
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Arya AD, Verma SS, Chakarabarti P, Chakrabarti T, Elngar AA, Kamali AM, Nami M. A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease. Brain Inform 2023; 10:17. [PMID: 37450224 PMCID: PMC10349019 DOI: 10.1186/s40708-023-00195-7] [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: 12/27/2022] [Accepted: 06/02/2023] [Indexed: 07/18/2023] Open
Abstract
Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.
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Affiliation(s)
| | | | | | | | - Ahmed A. Elngar
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, 62511 Egypt
| | - Ali-Mohammad Kamali
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Nami
- Cognitive Neuropsychology Unit, Department of Social Sciences, Canadian University Dubai, Dubai, UAE
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14
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Ukwuoma CC, Cai D, Heyat MBB, Bamisile O, Adun H, Al-Huda Z, Al-Antari MA. Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101596. [PMID: 37275558 PMCID: PMC10211254 DOI: 10.1016/j.jksuci.2023.101596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Abstract
COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.
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Affiliation(s)
- Chiagoziem C Ukwuoma
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Dongsheng Cai
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Olusola Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, China
| | - Humphrey Adun
- Department of Mechanical and Energy Systems Engineering, Cyprus International University, Nicosia, North Nicosia, Cyprus
| | - Zaid Al-Huda
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - Mugahed A Al-Antari
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
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15
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Venkatachala Appa Swamy M, Periyasamy J, Thangavel M, Khan SB, Almusharraf A, Santhanam P, Ramaraj V, Elsisi M. Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction. Diagnostics (Basel) 2023; 13:diagnostics13111942. [PMID: 37296794 DOI: 10.3390/diagnostics13111942] [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: 03/07/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023] Open
Abstract
With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.
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Affiliation(s)
| | - Jayalakshmi Periyasamy
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Muthamilselvan Thangavel
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Surbhi B Khan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Department of Data Science, School of Science, Engineering and Environment, University of Sanford, Manchester M5 4WT, UK
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Prasanna Santhanam
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Vijayan Ramaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Mahmoud Elsisi
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
- Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., Cairo P.O. Box 11241, Egypt
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16
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Kumari J, Kumar E, Kumar D. A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-29. [PMID: 37359744 PMCID: PMC10064607 DOI: 10.1007/s11831-023-09915-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare include Predictive Analytics, Medical Image Analysis, Drug Discovery, Personalized Medicine, and Electronic Health Records (EHR) Analysis. It has become one of the advanced and popular tool for computer science domain.' The advancement of ML and DL for various fields has opened new avenues for research and development. It could revolutionize prediction and decision-making capabilities. Due to increased awareness about the ML and DL in the healthcare, it has become one of the vital approaches for the sector. High-volume of unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble for healthcare sector. The current study uses analysis to examine research trends in adoption of machine learning and deep learning approaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCI journals are used as the datasets for the comprehensive analysis. Apart from these various search strategy are utilised for the requisite scientific analysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and author based analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the current study will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.
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Affiliation(s)
- Juli Kumari
- Indira Gandhi Delhi Technical University for Women (IGDTUW), New Church Rd, Kashmere Gate, Delhi, James Church, New Delhi, 110006 India
| | - Ela Kumar
- Indira Gandhi Delhi Technical University for Women (IGDTUW), New Church Rd, Kashmere Gate, Delhi, James Church, New Delhi, 110006 India
| | - Deepak Kumar
- Center of Excellence in Weather & Climate Analytics, Atmospheric Sciences Research Center (ASRC), University at Albany (UAlbany), State University of New York (SUNY), Albany, New York 12226 USA
- Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Buddha Nagar, Noida, Uttar Pradesh 201313 India
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17
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Illakiya T, Karthik R. Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives. Neuroinformatics 2023; 21:339-364. [PMID: 36884142 DOI: 10.1007/s12021-023-09625-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2023] [Indexed: 03/09/2023]
Abstract
Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the radiologists in producing accurate results toward effective disease diagnosis. The objective of this research is to highlight the importance of deep learning models in the detection of Alzheimer's Disease (AD). The main objective of this research is to analyze different deep learning methods used for detecting AD. This study examines 103 research articles published in various research databases. These articles have been selected based on specific criteria to find the most relevant findings in the field of AD detection. The review was carried out based on deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL). To propose accurate methods for the detection, segmentation, and severity grading of AD, the radiological features need to be examined in greater depth. This review attempts to analyze different deep learning methods applied for AD detection using neuroimaging modalities like Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), etc. The focus of this review is restricted to deep learning works based on radiological imaging data for AD detection. There are a few works that have utilized other biomarkers to understand the effect of AD. Also, articles published in English were alone considered for analysis. This work concludes by highlighting the key research issues towards effective AD detection. Though several methods have yielded promising results in AD detection, the progression from Mild Cognitive Impairment (MCI) to AD need to be analyzed in greater depth using DL models.
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Affiliation(s)
- T Illakiya
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - R Karthik
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
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18
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Mohammed A, Kora R. A Comprehensive Review on Ensemble Deep Learning: Opportunities and Challenges. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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19
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Liu F, Yuan S, Li W, Xu Q, Sheng B. Patch-based deep multi-modal learning framework for Alzheimer’s disease diagnosis using multi-view neuroimaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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An Optimization Numerical Spiking Neural P System for Solving Constrained Optimization Problems. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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21
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Subramanyam Rallabandi V, Seetharaman K. Classification of cognitively normal controls, mild cognitive impairment and Alzheimer’s disease using transfer learning approach. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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22
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Arco JE, Ortiz A, Castillo-Barnes D, Górriz JM, Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.109991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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23
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Bhasin H, Agrawal RK. Triploid genetic algorithm for convolutional neural network-based diagnosis of mild cognitive impairment. Alzheimers Dement 2022; 18:2283-2291. [PMID: 35103391 DOI: 10.1002/alz.12565] [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: 07/05/2021] [Accepted: 12/03/2021] [Indexed: 01/31/2023]
Abstract
The diagnosis of mild cognitive impairment (MCI), which is deemed a formative phase of dementia, may greatly assist clinicians in delaying its headway toward dementia. This article proposes a deep learning approach based on a triploid genetic algorithm, a proposed variant of genetic algorithms, for classifying MCI converts and non-converts using structural magnetic resonance imaging data. It also explores the effect of the choice of activation functions and that of the selection of hyper-parameters on the performance of the model. The proposed work is a step toward automated convolutional neural networks. The performance of the proposed method is measured in terms of accuracy and empirical studies exhibit the preeminence of our proposed method over the existing ones. The proposed model results in a maximum accuracy of 0.97961. Thus, it may contribute to the effective diagnosis of MCI and may prove important in clinical settings.
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Affiliation(s)
- Harsh Bhasin
- School of Computer and Systems Sciences, Jawaharlal Nehru University, Delhi, India
| | - R K Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, Delhi, India
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- School of Computer and Systems Sciences, Jawaharlal Nehru University, Delhi, India
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24
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Li M, Liu Y, Liu Y, Pu C, Yin R, Zeng Z, Deng L, Wang X. Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity. Front Physiol 2022; 13:956254. [PMID: 36299253 PMCID: PMC9589234 DOI: 10.3389/fphys.2022.956254] [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: 05/30/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70–0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65–0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.
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Affiliation(s)
- Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yan Liu
- Second Clinical Medical College, Nanchang University, Nanchang, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang, China
| | - Ruocheng Yin
- Queen Mary College, Nanchang University, Nanchang, China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
- School of Public Health, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
- *Correspondence: Libin Deng, ; Xing Wang,
| | - Xing Wang
- School of Life Sciences, Nanchang University, Nanchang, China
- Clinical Medical Experiment Center, Nanchang University, Nanchang, China
- *Correspondence: Libin Deng, ; Xing Wang,
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25
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Ileri R, Latifoğlu F, Demirci E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput 2022; 60:3041-3055. [DOI: 10.1007/s11517-022-02656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/19/2022] [Indexed: 10/14/2022]
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26
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Shobana M, Balasraswathi VR, Radhika R, Oleiwi AK, Chaudhury S, Ladkat AS, Naved M, Rahmani AW. Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9900668. [PMID: 35937383 PMCID: PMC9348925 DOI: 10.1155/2022/9900668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/30/2022] [Accepted: 07/14/2022] [Indexed: 11/18/2022]
Abstract
Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.
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Affiliation(s)
- M. Shobana
- SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, 603203, Chennai, India
| | - V. R. Balasraswathi
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
| | - R. Radhika
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
| | - Ahmed Kareem Oleiwi
- Department of Computer Technical Engineering, The Islamic University, 54001 Najaf, Iraq
| | | | - Ajay S. Ladkat
- Department of Instrumentation Engineering, Vishwakarma Institute of Technology, Pune, India
| | - Mohd Naved
- Amity International Business School (AIBS), Amity University, Noida, India
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27
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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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28
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Wang H, Feng T, Zhao Z, Bai X, Han G, Wang J, Dai Z, Wang R, Zhao W, Ren F, Gao F. Classification of Alzheimer's Disease Based on Deep Learning of Brain Structural and Metabolic Data. Front Aging Neurosci 2022; 14:927217. [PMID: 35903535 PMCID: PMC9315355 DOI: 10.3389/fnagi.2022.927217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
To improve the diagnosis and classification of Alzheimer's disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.
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Affiliation(s)
- Huiquan Wang
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Tianzi Feng
- School of Electrical and Information Engineering, Tiangong University, Tianjin, China
| | - Zhe Zhao
- School of Electrical and Information Engineering, Tiangong University, Tianjin, China
| | - Xue Bai
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Guang Han
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Jinhai Wang
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Zongrui Dai
- Westa College, Southwest University, Chongqing, China
| | - Rong Wang
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Weibiao Zhao
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Fuxin Ren
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Li C, Wang Q, Liu X, Hu B. An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer’s Disease Levels. Front Aging Neurosci 2022; 14:930584. [PMID: 35898323 PMCID: PMC9309569 DOI: 10.3389/fnagi.2022.930584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Detection of early morphological changes in the brain and early diagnosis are important for Alzheimer’s disease (AD), and high-resolution magnetic resonance imaging (MRI) can be used to help diagnose and predict the disease. In this paper, we proposed two improved ResNet algorithms that introduced the Contextual Transformer (CoT) module, group convolution, and Channel Shuffle mechanism into the traditional ResNet residual blocks. The CoT module is used to replace the 3 × 3 convolution in the residual block to enhance the feature extraction capability of the residual block, while the Channel Shuffle mechanism is used to reorganize the feature maps of different groups in the input layer to improve the communication between the feature maps from different groups. Images of 503 subjects, including 116 healthy controls (HC), 187 subjects with mild cognitive impairment (MCI), and 200 subjects with AD, were selected and collated from the ADNI database, and then, the data were pre-processed and sliced. After that, 10,060 slices were obtained and the three groups of AD, MCI and HC were classified using the improved algorithms. The experiments showed that the refined ResNet-18-based algorithm improved the top-1 accuracy by 2.06%, 0.33%, 1.82%, and 1.52% over the traditional ResNet-18 algorithm for four medical image classification tasks, namely AD: MCI, AD: HC, MCI: HC, and AD: MCI: HC, respectively. The enhanced ResNet-50-based algorithm improved the top-1 accuracy by 1.02%, 2.92%, 3.30%, and 1.31%, respectively, over the traditional ResNet-50 algorithm in four medical image classification tasks, demonstrating the effectiveness of the CoT module replacement and the inclusion of the channel shuffling mechanism, as well as the competitiveness of the improved algorithms.
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Affiliation(s)
- Chao Li
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Biomedical Spectroscopy of Xi’an, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
- Key Laboratory of Biomedical Spectroscopy of Xi’an, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
- *Correspondence: Quan Wang,
| | - Xuebin Liu
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
| | - Bingliang Hu
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
- Bingliang Hu,
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30
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Leming M, Das S, Im H. Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease. Artif Intell Med 2022; 129:102309. [DOI: 10.1016/j.artmed.2022.102309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 02/21/2022] [Accepted: 04/16/2022] [Indexed: 11/29/2022]
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31
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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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32
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Cura OK, Akan A, Yilmaz GC, Ture HS. Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods. Int J Neural Syst 2022; 32:2250042. [DOI: 10.1142/s0129065722500423] [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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33
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Hong X, Huang K, Lin J, Ye X, Wu G, Chen L, Chen E, Zhao S. Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification. Front Aging Neurosci 2022; 14:891433. [PMID: 35721019 PMCID: PMC9199857 DOI: 10.3389/fnagi.2022.891433] [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: 03/07/2022] [Accepted: 04/19/2022] [Indexed: 12/03/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.
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Affiliation(s)
- Xin Hong
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
- Key Laboratory of Computer Vision and Machine Learning (Huaqiao University), Fujian Province University, Xiamen, China
- *Correspondence: Xin Hong ;
| | - Kaifeng Huang
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Jie Lin
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Xiaoyan Ye
- Fuzhou Comvee Network and Technology Co., Ltd, Fuzhou, China
- Xiaoyan Ye
| | - Guoxiang Wu
- College of Foreign Languages, Huaqiao University, Quanzhou, China
| | - Longfei Chen
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - E. Chen
- Department of Neurosurgery, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
- E. Chen
| | - Siyu Zhao
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
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Saleem TJ, Zahra SR, Wu F, Alwakeel A, Alwakeel M, Jeribi F, Hijji M. Deep Learning-Based Diagnosis of Alzheimer's Disease. J Pers Med 2022; 12:815. [PMID: 35629237 PMCID: PMC9143671 DOI: 10.3390/jpm12050815] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
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Affiliation(s)
- Tausifa Jan Saleem
- Department of Computer Science and Engineering, National Institute of Technology Srinagar, Srinagar 190006, J&K, India; (T.J.S.); (S.R.Z.)
| | - Syed Rameem Zahra
- Department of Computer Science and Engineering, National Institute of Technology Srinagar, Srinagar 190006, J&K, India; (T.J.S.); (S.R.Z.)
| | - Fan Wu
- Department of Computer Science, Tuskegee University, Tuskegee, AL 36088, USA;
| | - Ahmed Alwakeel
- Sensor Network and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia; (M.A.); (M.H.)
| | - Mohammed Alwakeel
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia; (M.A.); (M.H.)
| | - Fathe Jeribi
- College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia;
| | - Mohammad Hijji
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia; (M.A.); (M.H.)
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Senthilkumar T, Kumarganesh S, Sivakumar P, Periyarselvam K. Primitive detection of Alzheimer’s disease using neuroimaging: A progression model for Alzheimer’s disease: Their applications, benefits, and drawbacks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Alzheimer’s disease (A.D.) is the most widespread type of Dementia, and it is not a curable neurodegenerative disease that affects millions of older people. Researchers were able to use their understanding of Alzheimer’s disease risk variables to develop enrichment processes for longitudinal imaging studies. Using this method, they reduced their sample size and study time. This paper describes the primitive detective of Alzheimer’s diseases using Neuroimaging techniques. Several preprocessing methods were used to ensure that the dataset was ready for subsequent feature extraction and categorization. The noise was reduced by converting and averaging many scan frames from real to DCT space. Both sides of the averaged image were filtered and combined into a single shot after being converted to real space. InceptionV3 and DenseNet201 are two pre-trained models used in the suggested model. The PCA approach was used to select the traits, and the resulting explained variance ratio was 0.99The Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC)—and UCI machine learning datasets showed that our method is faster and more successful at identifying complete long-risk patterns when compared to existing methods.
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Affiliation(s)
- T. Senthilkumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | | | - P. Sivakumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | - K. Periyarselvam
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
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36
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A CAD system design for Alzheimer's disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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37
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Rani P, Dutta K, Kumar V. Artificial intelligence techniques for prediction of drug synergy in malignant diseases: Past, present, and future. Comput Biol Med 2022; 144:105334. [DOI: 10.1016/j.compbiomed.2022.105334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 12/22/2022]
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38
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Deep learning architectures for Parkinson's disease detection by using multi-modal features. Comput Biol Med 2022; 146:105610. [DOI: 10.1016/j.compbiomed.2022.105610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022]
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39
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Castillo-Barnes D, Jimenez-Mesa C, Martinez-Murcia FJ, Salas-Gonzalez D, Ramírez J, Górriz JM. Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images. Int J Neural Syst 2022; 32:2250019. [PMID: 35313792 DOI: 10.1142/s0129065722500198] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson's Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.
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Affiliation(s)
- Diego Castillo-Barnes
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Francisco J Martinez-Murcia
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Diego Salas-Gonzalez
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Juan M Górriz
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain.,Department of Psychiatry, University of Cambridge, Herchel Smith Buidling for Brain & Mind Sciences, Forvie Site Robinson Way, Cambridge CB2 0SZ, UK
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Huang H, Zheng S, Yang Z, Wu Y, Li Y, Qiu J, Cheng Y, Lin P, Lin Y, Guan J, Mikulis DJ, Zhou T, Wu R. Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes. Cereb Cortex 2022; 33:754-763. [PMID: 35301516 PMCID: PMC9890469 DOI: 10.1093/cercor/bhac099] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 02/04/2023] Open
Abstract
This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.
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Affiliation(s)
- Huaidong Huang
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | | | - Zhongxian Yang
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, No. 1333, Xinhu Road, Bao'an District, Shenzhen 518000, China
| | - Yi Wu
- Department of Neurology, Shantou Central Hospital and Affiliated Shantou Hospital of Sun Yat-Sen University, No. 114, Waima Road, Jinping District, Shantou 515041, China
| | - Yan Li
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - Jinming Qiu
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - Yan Cheng
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - Panpan Lin
- School of Clinical Medicine, Quanzhou Medical College, No. 2, Anji Road, Luojiang District, Quanzhou 362000, China
| | - Yan Lin
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - Jitian Guan
- Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - David John Mikulis
- Division of Neuroradiology, Department of Medical Imaging, University of Toronto, University Health Network, Toronto Western Hospital, 399 Bathurst Street, Toronto, Ontario M5T 2S7, Canada
| | - Teng Zhou
- Department of Computer Science, Shantou University, 243 Daxue Road, Shantou 515063, China
- Renhua Wu, Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
| | - Renhua Wu
- Department of Computer Science, Shantou University, 243 Daxue Road, Shantou 515063, China
- Renhua Wu, Department of Medical Imaging, The 2nd Affiliated Hospital, Medical College of Shantou University, No. 69, Dongxia North Road, Jinping District, Shantou 515041, China
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41
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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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42
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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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43
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Kini AS, Gopal Reddy AN, Kaur M, Satheesh S, Singh J, Martinetz T, Alshazly H. Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7377502. [PMID: 35280708 PMCID: PMC8896964 DOI: 10.1155/2022/7377502] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022]
Abstract
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
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Affiliation(s)
- Anita S. Kini
- Manipal Institute of Technology MAHE, Manipal, Karnataka 576104, India
| | - A. Nanda Gopal Reddy
- Department of IT, Mahaveer Institute of Science and Technology, Hyderabad, Telangana 500005, India
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - S. Satheesh
- Department of Electronics and Communication Engineering, Malineni Lakshmaiah Women's Engineering College, Guntur, Andhra Pradesh 522017, India
| | - Jagendra Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida-203206, India
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck 23562, Germany
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
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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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Karaboğa MNS, Sezgintürk MK. Biosensor approaches on the diagnosis of neurodegenerative diseases: Sensing the past to the future. J Pharm Biomed Anal 2022; 209:114479. [PMID: 34861607 DOI: 10.1016/j.jpba.2021.114479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/05/2021] [Accepted: 11/14/2021] [Indexed: 12/12/2022]
Abstract
Early diagnosis of neurodegeneration-oriented diseases that develop with the aging world is essential for improving the patient's living conditions as well as the treatment of the disease. Alzheimer's and Parkinson's diseases are prominent examples of neurodegeneration characterized by dementia leading to the death of nerve cells. The clinical diagnosis of these diseases only after the symptoms appear, delays the treatment process. Detection of biomarkers, which are distinctive molecules in biological fluids, involved in neurodegeneration processes, has the potential to allow early diagnosis of neurodegenerative diseases. Studies on biosensors, whose main responsibility is to detect the target analyte with high specificity, has gained momentum in recent years with the aim of high detection of potential biomarkers of neurodegeneration process. This study aims to provide an overview of neuro-biosensors developed on the basis of biomarkers identified in biological fluids for the diagnosis of neurodegenerative diseases such as Alzheimer's disease (AD), and Parkinson's disease (PD), and to provide an overview of the urgent needs in this field, emphasizing the importance of early diagnosis in the general lines of the neurodegeneration pathway. In this review, biosensor systems developed for the detection of biomarkers of neurodegenerative diseases, especially in the last 5 years, are discussed.
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Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2793361. [PMID: 35154618 PMCID: PMC8831050 DOI: 10.1155/2022/2793361] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 01/12/2023]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
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47
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Rout SK, Sahani M, Dora C, Biswal PK, Biswal B. An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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48
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Sethi M, Ahuja S, Rani S, Koundal D, Zaguia A, Enbeyle W. An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8739960. [PMID: 35103240 PMCID: PMC8800619 DOI: 10.1155/2022/8739960] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
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Affiliation(s)
- Monika Sethi
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Sachin Ahuja
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia
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Zhang W, Zhang T, Pan T, Zhao S, Nie B, Liu H, Shan B. Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer's Disease. Front Aging Neurosci 2022; 13:764272. [PMID: 34975455 PMCID: PMC8715958 DOI: 10.3389/fnagi.2021.764272] [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: 08/25/2021] [Accepted: 11/01/2021] [Indexed: 01/17/2023] Open
Abstract
Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer’s disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment. Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t-test were used to explain the potential causes of uncertain cases. Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant (p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent (p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state (p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected (p < 0.05). Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.
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Affiliation(s)
- Wei Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Tingting Pan
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Shilun Zhao
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hua Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Baoci Shan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.,School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
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50
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Arco JE, Ortiz A, Ramírez J, Zhang YD, Górriz JM. Tiled Sparse Coding in Eigenspaces for Image Classification. Int J Neural Syst 2021; 32:2250007. [PMID: 34967705 DOI: 10.1142/s0129065722500071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.
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Affiliation(s)
- Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Malaga 29010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
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