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Goyal P, Rani R, Singh K. An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning. J Neural Transm (Vienna) 2024:10.1007/s00702-024-02830-x. [PMID: 39249515 DOI: 10.1007/s00702-024-02830-x] [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: 04/19/2024] [Accepted: 08/22/2024] [Indexed: 09/10/2024]
Abstract
Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders substantially impact the quality of life and burden healthcare systems and society. The demographic characteristics, and machine learning approaches have now been employed to diagnose these illnesses; however, they possess accuracy limitations. Therefore, the authors have developed ranking-based ensemble approach based on the weighted strategy of deep learning classifiers. The whole modeling procedure of the proposed approach incorporates three phases. In phase I, preprocessing techniques are applied to clean the noise in datasets to make it standardized according to deep learning models as it significantly impacts their performance. In phase II, five deep learning models are selected for classification and calculation of prediction results. In phase III, a ranking-based ensemble approach is proposed to ensemble the results of the five models after calculating the ranks and weights of them. In addition, the Magnetic Resonance Imaging (MRI) datasets named Alzheimer's Disease Neuroimaging Initiative (ADNI) for AD classification and Parkinson's Progressive Marker Initiative (PPMI) for PD classification are selected to validate the proposed approach. Furthermore, the proposed method achieved the classification accuracy on AD- Cognitive Normals (CN) at 97.89%, AD- Mild Cognitive Impairment (MCI) at 99.33% and CN-MCI at 99.44% and on PD-CN at 99.22%, PD- Scans Without Evidence of Dopaminergic Effect (SWEDD) at 97.56% and CN-SWEDD at 98.22% respectively. Also, the multi-class classification shows the promising accuracy of 97.18% for AD and 97.85% for PD for the proposed framework. The findings of the study show that the proposed deep learning-based ensemble technique is competitive for AD and PD prediction in both multiclass and binary class classification. Furthermore, the proposed approach enhances generalization performance in diagnosing neurodegenerative diseases and performs better than existing approaches.
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Affiliation(s)
- Palak Goyal
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India.
| | - Rinkle Rani
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India
| | - Karamjeet Singh
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India
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Shaffi N, Subramanian K, Vimbi V, Hajamohideen F, Abdesselam A, Mahmud M. Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease. Int J Neural Syst 2024; 34:2450029. [PMID: 38576308 DOI: 10.1142/s0129065724500291] [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: 04/06/2024]
Abstract
Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.
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Affiliation(s)
- Noushath Shaffi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Karthikeyan Subramanian
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Viswan Vimbi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Faizal Hajamohideen
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Abdelhamid Abdesselam
- Department of Computer Science, College of Science, Sultan Qaboos University, P.O. Box: 36, Al-Khod 123, Sultanate of Oman, Oman
| | - Mufti Mahmud
- Department of Computer Science, Medical Technologies Innovation Facility and Centre for Computer Science and Informatics (CIRC), Nottingham Trent University, Nottingham NG11 8NS, UK
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3
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Jenber Belay A, Walle YM, Haile MB. Deep Ensemble learning and quantum machine learning approach for Alzheimer's disease detection. Sci Rep 2024; 14:14196. [PMID: 38902368 PMCID: PMC11189917 DOI: 10.1038/s41598-024-61452-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/06/2024] [Indexed: 06/22/2024] Open
Abstract
Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer's disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer's disease. The Alzheimer's disease Neuroimaging Initiative I and Alzheimer's disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer's disease by registering an accuracy of 99.89 and 98.37 F1-score.
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Affiliation(s)
- Abebech Jenber Belay
- Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia.
| | - Yelkal Mulualem Walle
- Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia
| | - Melaku Bitew Haile
- Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia
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4
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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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5
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Vimbi V, Shaffi N, Mahmud M. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection. Brain Inform 2024; 11:10. [PMID: 38578524 PMCID: PMC10997568 DOI: 10.1186/s40708-024-00222-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 03/04/2024] [Indexed: 04/06/2024] Open
Abstract
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer's disease (AD). Adhering to PRISMA and Kitchenham's guidelines, we identified 23 relevant articles and investigated these frameworks' prospective capabilities, benefits, and challenges in depth. The results emphasise XAI's crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.
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Affiliation(s)
- Viswan Vimbi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, OM 311, Sohar, Sultanate of Oman
| | - Noushath Shaffi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, OM 311, Sohar, Sultanate of Oman
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, UK.
- Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham, NG11 8NS, UK.
- Computing and Informatics Research Centre, Nottingham Trent University, Nottingham, NG11 8NS, UK.
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6
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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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Hajamohideen F, Shaffi N, Mahmud M, Subramanian K, Al Sariri A, Vimbi V, Abdesselam A. Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function. Brain Inform 2023; 10:5. [PMID: 36806042 PMCID: PMC9937523 DOI: 10.1186/s40708-023-00184-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/03/2023] [Indexed: 02/19/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.
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Affiliation(s)
- Faizal Hajamohideen
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
| | - Noushath Shaffi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
| | - Karthikeyan Subramanian
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
| | - Arwa Al Sariri
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
| | - Viswan Vimbi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
| | - Abdelhamid Abdesselam
- Department of Computer Science, Sultan Qaboos University, 123 Muscat, Sultanate of Oman
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- College of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311 Sohar, Sultanate of Oman
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS Nottingham, UK
- Department of Computer Science, Sultan Qaboos University, 123 Muscat, Sultanate of Oman
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A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10090-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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9
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Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Chen T, Su P, Shen Y, Chen L, Mahmud M, Zhao Y, Antoniou G. A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia. Front Neurosci 2022; 16:867664. [PMID: 35979331 PMCID: PMC9376621 DOI: 10.3389/fnins.2022.867664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.
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Affiliation(s)
- Tianhua Chen
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Pan Su
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Yinghua Shen
- School of Economics and Business Administration, Chongqing University, Chongqing, China
| | - Lu Chen
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Grigoris Antoniou
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
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Ghaffari H, Tavakoli H, Pirzad Jahromi G. Deep transfer learning-based fully automated detection and classification of Alzheimer's disease on brain MRI. Br J Radiol 2022; 95:20211253. [PMID: 35616643 PMCID: PMC10162060 DOI: 10.1259/bjr.20211253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/24/2022] [Accepted: 05/19/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To employ different automated convolutional neural network (CNN)-based transfer learning (TL) methods for both binary and multiclass classification of Alzheimer's disease (AD) using brain MRI. METHODS Herein, we applied three popular pre-trained CNN models (ResNet101, Xception, and InceptionV3) using a fine-tuned approach of TL on 3D T1-weighted brain MRI from a subset of ADNI dataset (n = 305 subjects). To evaluate power of TL, the aforementioned networks were also trained from scratch for performance comparison. Initially, Unet network segmentedthe MRI scans into characteristic components of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The proposed networks were trained and tested over the pre-processed and augmented segmented and whole images for both binary (NC/AD + progressive mild cognitive impairment (pMCI)+stable MCI (sMCI)) and 4-class (AD/pMCI/sMCI/NC) classification. Also, two independent test sets from the OASIS (n = 30) and AIBL (n = 60) datasets were used to externally assess the performance of the proposed algorithms. RESULTS The proposed TL-based CNN models achieved better performance compared to the training CNN models from scratch. On the ADNI test set, InceptionV3-TL achieved the highest accuracy of 93.75% and AUC of 92.0% for binary classification, as well as the highest accuracy of 93.75% and AUC of 96.0% for multiclass classification of AD on the whole images. On the OASIS test set, InceptionV3-TL outperformed two other models by achieving 93.33% accuracy with 93.0% AUC in binary classification of AD on the whole images. On the AIBL test set, InceptionV3-TL also outperformed two other models in both binary and multiclass classification tasks on the whole MR images and achieved accuracy/AUC of 93.33%/95.0% and 90.0%/93.0%, respectively. The GM segment as input provided the highest performance in both binary and multiclass classification of AD, as compared to the WM and CSF segments. CONCLUSION This study demonstrates the potential of applying deep TL approach for automated detection and classification of AD using brain MRI with high accuracy and robustness across internal and external test data, suggesting that these models can possibly be used as a supportive tool to assist clinicians in creating objective opinion and correct diagnosis. ADVANCES IN KNOWLEDGE We used CNN-based TL approaches and the augmentation techniques to overcome the insufficient data problem. Our study provides evidence that deep TL algorithms can be used for both binary and multiclass classification of AD with high accuracy.
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Affiliation(s)
- Hamed Ghaffari
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Gila Pirzad Jahromi
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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12
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Fouladi S, Safaei AA, Mammone N, Ghaderi F, Ebadi MJ. Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10033-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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13
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Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer’s Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5261942. [PMID: 35419043 PMCID: PMC8995544 DOI: 10.1155/2022/5261942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/27/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022]
Abstract
Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.
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14
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Mehta NK, Prasad SS, Saurav S, Saini R, Singh S. Three-dimensional DenseNet self-attention neural network for automatic detection of student's engagement. APPL INTELL 2022; 52:13803-13823. [PMID: 35340984 PMCID: PMC8932470 DOI: 10.1007/s10489-022-03200-4] [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] [Accepted: 01/04/2022] [Indexed: 01/17/2023]
Abstract
Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students' understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.
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Affiliation(s)
- Naval Kishore Mehta
- Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India
- CSIR-Central Electronics Engineering Research Institute(CSIR-CEERI), Pilani, India
| | - Shyam Sunder Prasad
- Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India
- CSIR-Central Electronics Engineering Research Institute(CSIR-CEERI), Pilani, India
| | - Sumeet Saurav
- Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India
- CSIR-Central Electronics Engineering Research Institute(CSIR-CEERI), Pilani, India
| | - Ravi Saini
- Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India
- CSIR-Central Electronics Engineering Research Institute(CSIR-CEERI), Pilani, India
| | - Sanjay Singh
- Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India
- CSIR-Central Electronics Engineering Research Institute(CSIR-CEERI), Pilani, India
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15
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Odusami M, Maskeliūnas R, Damaševičius R. An Intelligent System for Early Recognition of Alzheimer's Disease Using Neuroimaging. SENSORS (BASEL, SWITZERLAND) 2022; 22:740. [PMID: 35161486 PMCID: PMC8839926 DOI: 10.3390/s22030740] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 05/08/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
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16
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Fabietti M, Mahmud M, Lotfi A. Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. Brain Inform 2022; 9:1. [PMID: 34997378 PMCID: PMC8741911 DOI: 10.1186/s40708-021-00149-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/23/2021] [Indexed: 11/13/2022] Open
Abstract
Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK. .,Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK. .,Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK
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17
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Paul A, Basu A, Mahmud M, Kaiser MS, Sarkar R. Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays. Neural Comput Appl 2022; 35:1-15. [PMID: 35013650 PMCID: PMC8729326 DOI: 10.1007/s00521-021-06737-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/21/2021] [Indexed: 12/20/2022]
Abstract
Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients' lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.
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Affiliation(s)
- Ashis Paul
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032 India
| | - Arpan Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032 India
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton, Nottingham NG11 8NS UK
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton, Nottingham NG11 8NS UK
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton, Nottingham NG11 8NS UK
| | - M. Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Dhaka, 1342 Bangladesh
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032 India
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18
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Sun X, Guo W, Shen J. Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data. Front Neurosci 2022; 16:1043626. [PMID: 36741058 PMCID: PMC9889549 DOI: 10.3389/fnins.2022.1043626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/12/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Brain degeneration is commonly caused by some chronic diseases, such as Alzheimer's disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data). Methods Several deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results. Results As different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively. Discussion The prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods.
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Affiliation(s)
- Xiaofei Sun
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Weiwei Guo
- EchoX Technology Limited, Hong Kong, Hong Kong SAR, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
- *Correspondence: Jing Shen,
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Shaffi N, Hajamohideen F, Mahmud M, Abdesselam A, Subramanian K, Sariri AA. Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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20
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Goyal B, Dogra A, Khoond R, Al-Turjman F. An Efficient Medical Assistive Diagnostic Algorithm for Visualisation of Structural and Tissue Details in CT and MRI Fusion. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09958-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Mahmud M, Kaiser MS, McGinnity TM, Hussain A. Deep Learning in Mining Biological Data. Cognit Comput 2021; 13:1-33. [PMID: 33425045 PMCID: PMC7783296 DOI: 10.1007/s12559-020-09773-x] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/28/2020] [Indexed: 02/06/2023]
Abstract
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures-known as deep learning (DL)-have been successfully applied to solve many complex pattern recognition problems. To investigate how DL-especially its different architectures-has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.
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Affiliation(s)
- Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK
- Medical Technology Innovation Facility, Nottingham Trent University, NG11 8NS Clifton, Nottingham, UK
| | - M. Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar 1342 Dhaka, Bangladesh
| | - T. Martin McGinnity
- Department of Computer Science, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK
- Intelligent Systems Research Centre, Ulster University, Northern Ireland BT48 7JL Derry, UK
| | - Amir Hussain
- School of Computing , Edinburgh, EH11 4BN Edinburgh, UK
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22
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Yadav KS, Miyapuram KP. A Novel Approach Towards Early Detection of Alzheimer’s Disease Using Deep Learning on Magnetic Resonance Images. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_43] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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23
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Fabietti M, Mahmud M, Lotfi A. A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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24
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25
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Indoor Navigation Support System for Patients with Neurodegenerative Diseases. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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