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Alsuhaibani M, Dodge HH, Mahoor MH. Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module. EXPERT SYSTEMS WITH APPLICATIONS 2024; 252:124185. [PMID: 38881832 PMCID: PMC11174143 DOI: 10.1016/j.eswa.2024.124185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Early detection of Mild Cognitive Impairment (MCI) leads to early interventions to slow the progression from MCI into dementia. Deep Learning (DL) algorithms could help achieve early non-invasive and low-cost detection of MCI. This paper presents the detection of MCI in older adults using DL models based only on facial features extracted from video-recorded conversations at home. We used the data collected from the I-CONECT behavioral intervention study (NCT02871921), where several sessions of semi-structured interviews between socially isolated older individuals and interviewers were video recorded. We develop a framework that extracts holistic spatial facial features using a convolutional autoencoder and temporal information using transformers. We proposed the Spatial-to-Temporal Attention Module (STAM) to detect the I-CONECT study participants' cognitive conditions (MCI vs. those with normal cognition (NC)) using facial and interaction features. The interaction features of the facial features improved the prediction performance compared with applying facial features solely. The detection accuracy using this combined method reached 88%, whereas the accuracy without applying the segments and sequences information of the facial features within a video on a certain theme was 84%. Overall, the results show that spatiotemporal facial features modeled using DL algorithms have a discriminating power for the detection of MCI.
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Affiliation(s)
- Muath Alsuhaibani
- Department of Electrical and Computer Engineering, University of Denver, Denver 80208, CO, United States
- Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Hiroko H. Dodge
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, United States
| | - Mohammad H. Mahoor
- Department of Electrical and Computer Engineering, University of Denver, Denver 80208, CO, United States
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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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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [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: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Awarayi NS, Twum F, Hayfron-Acquah JB, Owusu-Agyemang K. A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN. PLoS One 2024; 19:e0302358. [PMID: 38640105 PMCID: PMC11029622 DOI: 10.1371/journal.pone.0302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 04/03/2024] [Indexed: 04/21/2024] Open
Abstract
This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.
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Affiliation(s)
- Nicodemus Songose Awarayi
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana
| | - Frimpong Twum
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - James Ben Hayfron-Acquah
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Kwabena Owusu-Agyemang
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Augustine J, Jereesh AS. Identification of gene-level methylation for disease prediction. Interdiscip Sci 2023; 15:678-695. [PMID: 37603212 DOI: 10.1007/s12539-023-00584-w] [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: 02/17/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.
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Affiliation(s)
- Jisha Augustine
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Cochin, Kerala, 682022, India.
| | - A S Jereesh
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Cochin, Kerala, 682022, India
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Wang X, Zhao S, Pei Y, Luo Z, Xie L, Yan Y, Yin E. The increasing instance of negative emotion reduce the performance of emotion recognition. Front Hum Neurosci 2023; 17:1180533. [PMID: 37900730 PMCID: PMC10611512 DOI: 10.3389/fnhum.2023.1180533] [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: 03/06/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition. Methods A dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants. Results The results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and β frequency bands. Discussion This study provided new insight into the balance of emotion-inducing stimuli materials.
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Affiliation(s)
- Xiaomin Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Shaokai Zhao
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
| | - Yu Pei
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
| | - Zhiguo Luo
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
| | - Liang Xie
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China
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7
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Chaitanuwong P, Singhanetr P, Chainakul M, Arjkongharn N, Ruamviboonsuk P, Grzybowski A. Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies. Neurol Ther 2023; 12:1517-1532. [PMID: 37468682 PMCID: PMC10444735 DOI: 10.1007/s40120-023-00526-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.
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Affiliation(s)
- Pareena Chaitanuwong
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Panisa Singhanetr
- Mettapracharak Eye Institute, Mettapracharak (Wat Rai Khing) Hospital, Nakhon Pathom, Thailand
| | - Methaphon Chainakul
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Niracha Arjkongharn
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Paisan Ruamviboonsuk
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Andrzej Grzybowski
- Institute of Research in Ophthalmology, Foundation for Ophthalmology Development, Mickiewicza 24/3B, 60-836, Poznan, Poland.
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8
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Xu Z, Yang F, Wang H, Sun J, Zhu H, Wang S, Zhang Y. CGUFS: A clustering-guided unsupervised feature selection algorithm for gene expression data. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101731. [PMID: 38567001 PMCID: PMC7615789 DOI: 10.1016/j.jksuci.2023.101731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Aim Gene expression data is typically high dimensional with a limited number of samples and contain many features that are unrelated to the disease of interest. Existing unsupervised feature selection algorithms primarily focus on the significance of features in maintaining the data structure while not taking into account the redundancy among features. Determining the appropriate number of significant features is another challenge. Method In this paper, we propose a clustering-guided unsupervised feature selection (CGUFS) algorithm for gene expression data that addresses these problems. Our proposed algorithm introduces three improvements over existing algorithms. For the problem that existing clustering algorithms require artificially specifying the number of clusters, we propose an adaptive k-value strategy to assign appropriate pseudo-labels to each sample by iteratively updating a change function. For the problem that existing algorithms fail to consider the redundancy among features, we propose a feature grouping strategy to group highly redundant features. For the problem that the existing algorithms cannot filter the redundant features, we propose an adaptive filtering strategy to determine the feature combinations to be retained by calculating the potentially effective features and potentially redundant features of each feature group. Result Experimental results show that the average accuracy (ACC) and matthews correlation coefficient (MCC) indexes of the C4.5 classifier on the optimal features selected by the CGUFS algorithm reach 74.37% and 63.84%, respectively, significantly superior to the existing algorithms. Conclusion Similarly, the average ACC and MCC indexes of the Adaboost classifier on the optimal features selected by the CGUFS algorithm are significantly superior to the existing algorithms. In addition, statistical experiment results show significant differences between the CGUFS algorithm and the existing algorithms.
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Affiliation(s)
- Zhaozhao Xu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
| | - Fangyuan Yang
- Department of Gynecologic Oncology, The First Affiliated Hospital of Henan Polytechnic University, Jiaozuo, Henan 454000, China
| | - Hong Wang
- Department of Gynecologic Oncology, The First Affiliated Hospital of Henan Polytechnic University, Jiaozuo, Henan 454000, China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
| | - Hengde Zhu
- 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
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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Hamed A, Mohamed MF. A feature selection framework for anxiety disorder analysis using a novel multiview harris hawk optimization algorithm. Artif Intell Med 2023; 143:102605. [PMID: 37673574 DOI: 10.1016/j.artmed.2023.102605] [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: 07/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL2), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.
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Affiliation(s)
- Ahmed Hamed
- Department of Computer Science, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt.
| | - Marwa F Mohamed
- Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, 41522, Ismailia, Egypt
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10
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Hu Z, Li Y, Wang Z, Zhang S, Hou W. Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer's disease classification. Comput Biol Med 2023; 164:107304. [PMID: 37549456 DOI: 10.1016/j.compbiomed.2023.107304] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/14/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023]
Abstract
Deep learning (DL) algorithms based on brain MRI images have achieved great success in the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that of the most experienced clinical experts. As a novel feature fusion method, Transformer has achieved excellent performance in many computer vision tasks, which also greatly promotes the application of Transformer in medical images. However, when Transformer is used for 3D MRI image feature fusion, existing DL models treat the input local features equally, which is inconsistent with the fact that adjacent voxels have stronger semantic connections than spatially distant voxels. In addition, due to the relatively small size of the dataset for medical images, it is difficult to capture local lesion features in limited iterative training by treating all input features equally. This paper proposes a deep learning model Conv-Swinformer that focuses on extracting and integrating local fine-grained features. Conv-Swinformer consists of a CNN module and a Transformer encoder module. The CNN module summarizes the planar features of the MRI slices, and the Transformer module establishes semantic connections in 3D space for these planar features. By introducing the shift window attention mechanism in the Transformer encoder, the attention is focused on a small spatial area of the MRI image, which effectively reduces unnecessary background semantic information and enables the model to capture local features more accurately. In addition, the layer-by-layer enlarged attention window can further integrate local fine-grained features, thus enhancing the model's attention ability. Compared with DL algorithms that indiscriminately fuse local features of MRI images, Conv-Swinformer can fine-grained extract local lesion features, thus achieving better classification results.
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Affiliation(s)
- Zhentao Hu
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Yanyang Li
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Zheng Wang
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China.
| | - Shuo Zhang
- School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China
| | - Wei Hou
- College of Computer and Information Engineering, Henan University, Kaifeng, 475004, China
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11
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Jin Y, Ren Z, Wang W, Zhang Y, Zhou L, Yao X, Wu T. Classification of Alzheimer's disease using robust TabNet neural networks on genetic data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8358-8374. [PMID: 37161202 DOI: 10.3934/mbe.2023366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.
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Affiliation(s)
- Yu Jin
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zhe Ren
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Wenjie Wang
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yulei Zhang
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liang Zhou
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
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12
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Learning high-order interactions for polygenic risk prediction. PLoS One 2023; 18:e0281618. [PMID: 36763605 PMCID: PMC9916647 DOI: 10.1371/journal.pone.0281618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/27/2023] [Indexed: 02/11/2023] Open
Abstract
Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.
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Rajinikanth V, Vincent PMDR, Srinivasan K, Ananth Prabhu G, Chang CY. A framework to distinguish healthy/cancer renal CT images using the fused deep features. Front Public Health 2023; 11:1109236. [PMID: 36794074 PMCID: PMC9922737 DOI: 10.3389/fpubh.2023.1109236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 02/01/2023] Open
Abstract
Introduction Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management. Methods The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation. Results and discussion This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.
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Affiliation(s)
- Venkatesan Rajinikanth
- Division of Research and Innovation, Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India
| | - P. M. Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - G. Ananth Prabhu
- Department of Computer Science Engineering, Sahyadri College of Engineering and Management, Mangaluru, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
- Service Systems Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
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14
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Zhao H, Cao J, Xie J, Liao WH, Lei Y, Cao H, Qu Q, Bowen C. Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review. Digit Health 2023; 9:20552076231173569. [PMID: 37214662 PMCID: PMC10192816 DOI: 10.1177/20552076231173569] [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: 02/10/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Huan Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junyi Cao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junxiao Xie
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation
Engineering, The Chinese University of Hong
Kong, Shatin, N.T., Hong Kong, China
| | - Yaguo Lei
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Hongmei Cao
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Qiumin Qu
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Chris Bowen
- Department of Mechanical Engineering, University of Bath, Bath, UK
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15
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Pan X, Zhang G, Lin A, Guan X, Chen P, Ge Y, Chen X. An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm. Comput Biol Med 2022; 151:106229. [PMID: 36308897 DOI: 10.1016/j.compbiomed.2022.106229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/08/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%.
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Affiliation(s)
- Xiaotian Pan
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China.
| | - Guodao Zhang
- School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Aiju Lin
- College of international Education, Wenzhou University, Wenzhou 325035, China.
| | - Xiaochun Guan
- Department of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
| | - PingKuo Chen
- Great Bay University, Dongguan City 523000, China.
| | - Yisu Ge
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325100, China.
| | - Xin Chen
- Orthopedics Department of The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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16
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Li J, Xu H, Yu H, Jiang Z, Zhu L. Multi-modal feature selection with anchor graph for Alzheimer's disease. Front Neurosci 2022; 16:1036244. [DOI: 10.3389/fnins.2022.1036244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/21/2022] [Indexed: 11/10/2022] Open
Abstract
In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.
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17
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Amin J, Sharif M, Mallah GA, Fernandes SL. An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification. Front Public Health 2022; 10:969268. [PMID: 36148344 PMCID: PMC9486170 DOI: 10.3389/fpubh.2022.969268] [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: 06/14/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023] Open
Abstract
Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.
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Affiliation(s)
- Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan,*Correspondence: Javeria Amin
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ghulam Ali Mallah
- Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan
| | - Steven L. Fernandes
- Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, United States
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18
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Sun D, Peng H, Wu Z. Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network. Front Aging Neurosci 2022; 14:921906. [PMID: 35847663 PMCID: PMC9280980 DOI: 10.3389/fnagi.2022.921906] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 06/01/2022] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.
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19
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Tufail AB, Anwar N, Othman MTB, Ullah I, Khan RA, Ma YK, Adhikari D, Rehman AU, Shafiq M, Hamam H. Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22124609. [PMID: 35746389 PMCID: PMC9230850 DOI: 10.3390/s22124609] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/06/2022] [Accepted: 06/15/2022] [Indexed: 05/27/2023]
Abstract
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.
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Affiliation(s)
- Ahsan Bin Tufail
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (A.B.T.); (Y.-K.M.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
| | - Nazish Anwar
- Registered Medical Practitioner, Pakistan Medical Commission, Islamabad 44000, Pakistan;
| | - Mohamed Tahar Ben Othman
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Inam Ullah
- College of Internet of Things (IoT) Engineering, Changzhou Campus, Hohai University (HHU), Changzhou 213022, China;
| | - Rehan Ali Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan;
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (A.B.T.); (Y.-K.M.)
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Ateeq Ur Rehman
- Department of Electrical Engineering, Government College University Lahore, Lahore 54000, Pakistan;
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Habib Hamam
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada;
- International Institute of Technology and Management, Libreville BP1989, Gabon
- Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
- Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
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20
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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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21
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Baghdadi NA, Malki A, Balaha HM, Badawy M, Elhosseini M. A 3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22114250. [PMID: 35684871 PMCID: PMC9185328 DOI: 10.3390/s22114250] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/24/2022] [Accepted: 05/28/2022] [Indexed: 05/10/2023]
Abstract
Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.M.); (M.E.)
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
- Correspondence:
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.M.); (M.E.)
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
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22
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An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. Comput Biol Med 2022; 147:105675. [PMID: 35687926 DOI: 10.1016/j.compbiomed.2022.105675] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 11/22/2022]
Abstract
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
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24
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Bahado-Singh RO, Radhakrishna U, Gordevičius J, Aydas B, Yilmaz A, Jafar F, Imam K, Maddens M, Challapalli K, Metpally RP, Berrettini WH, Crist RC, Graham SF, Vishweswaraiah S. Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease. Cells 2022; 11:cells11111744. [PMID: 35681440 PMCID: PMC9179874 DOI: 10.3390/cells11111744] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.
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Affiliation(s)
- Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
| | - Juozas Gordevičius
- Vugene, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA;
| | - Buket Aydas
- Department of Care Management Analytics, Blue Cross Blue Shield of Michigan, Detroit, MI 48226, USA;
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Faryal Jafar
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Khaled Imam
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Michael Maddens
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Kshetra Challapalli
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Raghu P. Metpally
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
| | - Wade H. Berrettini
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Richard C. Crist
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
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Li F, Zhou Y, Zhang Y, Yin J, Qiu Y, Gao J, Zhu F. POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. Brief Bioinform 2022; 23:6532538. [PMID: 35183059 DOI: 10.1093/bib/bbac040] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 12/17/2022] Open
Abstract
Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.
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Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Jianqing Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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