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Kim M, Jang S, Lee D, Lee S, Gwak J, Jun SC, Kim JG. A comprehensive research setup for monitoring Alzheimer's disease using EEG, fNIRS, and Gait analysis. Biomed Eng Lett 2024; 14:13-21. [PMID: 38186957 PMCID: PMC10769970 DOI: 10.1007/s13534-023-00306-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/10/2023] [Accepted: 07/12/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease (AD) has a detrimental impact on brain function, affecting various aspects such as cognition, memory, language, and motor skills. Previous research has dominantly used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to individually measure brain signals or combine the two methods to target specific brain functions. However, comprehending Alzheimer's disease requires monitoring various brain functions rather than focusing on a single function. This paper presents a comprehensive research setup for a monitoring platform for AD. The platform incorporates a 32-channel dry electrode EEG, a custom-built four-channel fNIRS, and gait monitoring using a depth camera and pressure sensor. Various tasks are employed to target multiple brain functions. The paper introduced the detailed instrumentation of the fNIRS system, which measures the prefrontal cortex, outlines the experimental design targeting various brain functioning programmed in BCI2000 for visualizing EEG signals synchronized with experimental stimulation, and describes the gait monitoring hardware and software and protocol design. The ultimate goal of this platform is to develop an easy-to-perform brain and gait monitoring method for elderly individuals and patients with Alzheimer's disease. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00306-7.
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Affiliation(s)
- Minhee Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005 Republic of Korea
| | - Sehyeon Jang
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005 Republic of Korea
| | - Donjung Lee
- Korea Photonics Technology Institute, Gwangju, 61007 Republic of Korea
| | - Seungchan Lee
- Department of Medical Device, Korea Institute of Machinery & Materials, Daegu, 42994 Republic of Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469 Republic of Korea
| | - Sung Chan Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005 Republic of Korea
| | - Jae Gwan Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005 Republic of Korea
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2
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Farooq MU, Ullah Z, Khan A, Gwak J. DC-AAE: Dual channel adversarial autoencoder with multitask learning for KL-grade classification in knee radiographs. Comput Biol Med 2023; 167:107570. [PMID: 37897960 DOI: 10.1016/j.compbiomed.2023.107570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/25/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023]
Abstract
Knee osteoarthritis (OA) is a frequent musculoskeletal disorder that leads to physical disability in older adults. Manual OA assessment is performed via visual inspection, which is highly subjective as it suffers from moderate to high inter-observer variability. Many deep learning-based techniques have been proposed to address this issue. However, owing to the limited amount of labelled data, all existing solutions have limitations in terms of performance or the number of classes. This paper proposes a novel fully automatic Kellgren and Lawrence (KL) grade classification scheme in knee radiographs. We developed a semi-supervised multi-task learning-based approach that enables the exploitation of additional unlabelled data in an unsupervised as well as supervised manner. Specifically, we propose a dual-channel adversarial autoencoder, which is first trained in an unsupervised manner for reconstruction tasks only. To exploit the additional data in a supervised way, we propose a multi-task learning framework by introducing an auxiliary task. In particular, we use leg side identification as an auxiliary task, which allows the use of more datasets, e.g., CHECK dataset. The work demonstrates that the utilization of additional data can improve the primary task of KL-grade classification for which only limited labelled data is available. This semi-supervised learning essentially helps to improve the feature learning ability of our framework, which leads to improved performance for KL-grade classification. We rigorously evaluated our proposed model on the two largest publicly available datasets for various aspects, i.e., overall performance, the effect of additional unlabelled samples and auxiliary tasks, robustness analysis, and ablation study. The proposed model achieved the accuracy, precision, recall, and F1 score of 75.53%, 74.1%, 78.51%, and 75.34%, respectively. Furthermore, the experimental results show that the suggested model not only achieves state-of-the-art performance on two publicly available datasets but also exhibits remarkable robustness.
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Affiliation(s)
- Muhammad Umar Farooq
- Department of IT, Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Asifullah Khan
- Pattern Recognition Lab, DCIS, PIEAS, Nilore, Islamabad 45650, Pakistan
| | - Jeonghwan Gwak
- Department of IT, Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea; Department of Software, Korea National University of Transportation, Chungju 27469, South Korea; Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea; Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea.
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3
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Rauf Z, Khan AR, Sohail A, Alquhayz H, Gwak J, Khan A. Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN. Sci Rep 2023; 13:14047. [PMID: 37640739 PMCID: PMC10462751 DOI: 10.1038/s41598-023-40581-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 08/13/2023] [Indexed: 08/31/2023] Open
Abstract
Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN "BCF-Lym-Detector" for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed "BCF-Lym-Detector" generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed "BCF-Lym-Detector" show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique's generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists' assistance.
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Affiliation(s)
- Zunaira Rauf
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Abdul Rehman Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Hani Alquhayz
- Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, 11952, Al-Majmaah, Saudi Arabia
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, Republic of Korea.
| | - Asifullah Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan.
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan.
- Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan.
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Ullah Z, Usman M, Latif S, Khan A, Gwak J. SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation. Sci Rep 2023; 13:9087. [PMID: 37277554 DOI: 10.1038/s41598-023-36311-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023] Open
Abstract
Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Siddique Latif
- Faculty of Health and Computing, University of Southern Queensland, Toowoomba, QL, 4300, Australia
| | - Asifullah Khan
- Pattern Recognition Lab, DCIS, PIEAS, Nilore, Islamabad, 45650, Pakistan
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, 27469, South Korea.
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Ullah Z, Usman M, Gwak J. MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. Expert Syst Appl 2023; 216:119475. [PMID: 36619348 PMCID: PMC9810379 DOI: 10.1016/j.eswa.2022.119475] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/28/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
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6
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Farooq MU, Ullah Z, Gwak J. Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography. Comput Med Imaging Graph 2023; 104:102173. [PMID: 36641970 DOI: 10.1016/j.compmedimag.2022.102173] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/12/2022] [Accepted: 12/27/2022] [Indexed: 01/11/2023]
Abstract
Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation.
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Affiliation(s)
- Muhammad Umar Farooq
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea.
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea.
| | - Jeonghwan Gwak
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea; Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea; Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea.
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7
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Rauf Z, Sohail A, Khan SH, Khan A, Gwak J, Maqbool M. Attention-guided multi-scale deep object detection framework for lymphocyte analysis in IHC histological images. Microscopy (Oxf) 2023; 72:27-42. [PMID: 36239597 DOI: 10.1093/jmicro/dfac051] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 11/14/2022] Open
Abstract
Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to significant morphological variations, overlapping occurrence, artifact regions and high-class resemblance between clustered areas and artifacts. In this regard, a Lymphocyte Analysis Framework based on Deep Convolutional neural network (DC-Lym-AF) is proposed to analyze lymphocytes in immunohistochemistry images. The proposed framework comprises (i) pre-processing, (ii) screening phase, (iii) localization phase and (iv) post-processing. In the screening phase, a custom convolutional neural network architecture (lymphocyte dilated network) is developed to screen lymphocytic regions by performing a patch-level classification. This proposed architecture uses dilated convolutions and shortcut connections to capture multi-level variations and ensure reference-based learning. In contrast, the localization phase utilizes an attention-guided multi-scale lymphocyte detector to detect lymphocytes. The proposed detector extracts refined and multi-scale features by exploiting dilated convolutions, attention mechanism and feature pyramid network (FPN) using its custom attention-aware backbone. The proposed DC-Lym-AF shows exemplary performance on the NuClick dataset compared with the existing detection models, with an F-score and precision of 0.84 and 0.83, respectively. We verified the generalizability of our proposed framework by participating in a publically open LYON'19 challenge. Results in terms of detection rate (0.76) and F-score (0.73) suggest that the proposed DC-Lym-AF can effectively detect lymphocytes in immunohistochemistry-stained images collected from different laboratories. In addition, its promising generalization on several datasets implies that it can be turned into a medical diagnostic tool to investigate various histopathological problems. Graphical Abstract.
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Affiliation(s)
- Zunaira Rauf
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Department of Computer Science, Faculty of Computing and Artificial Intelligence, Air University, E-9, Islamabad 44230, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Department of Computer Systems Engineering, University of Engineering and Applied Sciences, Swat, Khyber Pakhtunkhwa 19130, Pakistan
| | - Asifullah Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Republic of Korea
| | - Muhammad Maqbool
- The University of Alabama at Birmingham, 1720 2nd Ave South, Birmingham, AL 35294, USA
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Ali Z, Mahmood T, Gwak J, Jan N. A novel extended Portuguese of Interactive and Multi‐Criteria Decision Making and Archimedean Bonferroni mean operators based on prospect theory to select green supplier with complex q‐rung orthopair fuzzy information. CAAI Trans on Intel Tech 2023. [DOI: 10.1049/cit2.12185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Affiliation(s)
- Zeeshan Ali
- Department of Mathematics & Statistics International Islamic University Islamabad Islamabad Pakistan
| | - Tahir Mahmood
- Department of Mathematics & Statistics International Islamic University Islamabad Islamabad Pakistan
| | - Jeonghwan Gwak
- Department of Software Korea National University of Transportation Chungju Korea
- Department of Biomedical Engineering Korea National University of Transportation Chungju Korea
- Department of AI Robotics Engineering Korea National University of Transportation Chungju Korea
- Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Chungju Korea
| | - Naeem Jan
- Department of Software Korea National University of Transportation Chungju Korea
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Gwak J, Garg H, Jan N, Akram B. A new approach to investigate the effects of artificial neural networks based on bipolar complex spherical fuzzy information. COMPLEX INTELL SYST 2023. [DOI: 10.1007/s40747-022-00959-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
AbstractArtificial neural network is revolutionizing business and everyday life, bringing us to the next level in artificial intelligence. It has a unique ability to extract meaning from complex data to find patterns and detect trends that are too convoluted for the human brain. This paper analyzes the artificial neural network impact on different computational organizations by using the innovative structure of bipolar complex spherical fuzzy relation which is any subset of the Cartesian product of two bipolar complex spherical fuzzy sets. This notion has a comprehensive structure that consists of membership grade, abstinence grade, and non-membership grade. Furthermore, various kinds of bipolar complex spherical fuzzy relation with suitable examples are given and some authentic results also have been proved. These newly defined structures are used to investigate the impact of artificial neural network work on a variety of organizations. The innovative framework is also compared with the existing structure in the field of fuzzy set theory to prove its superiority.
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Ullah Z, Usman M, Latif S, Gwak J. Densely attention mechanism based network for COVID-19 detection in chest X-rays. Sci Rep 2023; 13:261. [PMID: 36609667 PMCID: PMC9816547 DOI: 10.1038/s41598-022-27266-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
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Affiliation(s)
- Zahid Ullah
- grid.411661.50000 0000 9573 0030Department of Software, Korea National University of Transportation, Chungju, 27469 South Korea
| | - Muhammad Usman
- grid.31501.360000 0004 0470 5905Department of Computer Science and Engineering, Seoul National University, Seoul, 08826 South Korea
| | - Siddique Latif
- grid.1048.d0000 0004 0473 0844Faculty of Health and Computing, University of Southern Queensland, Toowoomba, QLD 4300 Australia
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea. .,Department of Biomedical Engineering, Korea National University of Transportation, Chungju, 27469, South Korea. .,Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, 27469, South Korea. .,Department of IT. Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, 27469, South Korea.
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Ullah Z, Usman M, Jeon M, Gwak J. Cascade multiscale residual attention CNNs with adaptive ROI for automatic brain tumor segmentation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Ho TKK, Gwak J. Feature-level ensemble approach for COVID-19 detection using chest X-ray images. PLoS One 2022; 17:e0268430. [PMID: 35834442 PMCID: PMC9282557 DOI: 10.1371/journal.pone.0268430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 05/01/2022] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.
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Affiliation(s)
- Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation, Chungju, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, South Korea
- * E-mail:
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Ho TKK, Kim M, Jeon Y, Kim BC, Kim JG, Lee KH, Song JI, Gwak J. Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy. Front Aging Neurosci 2022; 14:810125. [PMID: 35557842 PMCID: PMC9087351 DOI: 10.3389/fnagi.2022.810125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/01/2022] [Indexed: 12/28/2022] Open
Abstract
The timely diagnosis of Alzheimer’s disease (AD) and its prodromal stages is critically important for the patients, who manifest different neurodegenerative severity and progression risks, to take intervention and early symptomatic treatments before the brain damage is shaped. As one of the promising techniques, functional near-infrared spectroscopy (fNIRS) has been widely employed to support early-stage AD diagnosis. This study aims to validate the capability of fNIRS coupled with Deep Learning (DL) models for AD multi-class classification. First, a comprehensive experimental design, including the resting, cognitive, memory, and verbal tasks was conducted. Second, to precisely evaluate the AD progression, we thoroughly examined the change of hemodynamic responses measured in the prefrontal cortex among four subject groups and among genders. Then, we adopted a set of DL architectures on an extremely imbalanced fNIRS dataset. The results indicated that the statistical difference between subject groups did exist during memory and verbal tasks. This presented the correlation of the level of hemoglobin activation and the degree of AD severity. There was also a gender effect on the hemoglobin changes due to the functional stimulation in our study. Moreover, we demonstrated the potential of distinguished DL models, which boosted the multi-class classification performance. The highest accuracy was achieved by Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) using the original dataset of three hemoglobin types (0.909 ± 0.012 on average). Compared to conventional machine learning algorithms, DL models produced a better classification performance. These findings demonstrated the capability of DL frameworks on the imbalanced class distribution analysis and validated the great potential of fNIRS-based approaches to be further contributed to the development of AD diagnosis systems.
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Affiliation(s)
- Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation, Chungju, South Korea
| | - Minhee Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Younghun Jeon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Byeong C. Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Jae Gwan Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, South Korea
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
- Korea Brain Research Institute, Daegu, South Korea
| | - Jong-In Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, South Korea
- Department of IT and Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, South Korea
- *Correspondence: Jeonghwan Gwak, ;
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Ho TKK, Jeon Y, Na E, Ullah Z, Kim BC, Lee KH, Song J, Gwak J. DeepADNet: A CNN‐LSTM model for the multi‐class classification of Alzheimer’s disease using multichannel EEG. Alzheimers Dement 2021. [DOI: 10.1002/alz.057573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
| | - YoungHoon Jeon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Eunchan Na
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School Gwangju Republic of South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center, Chosun University Gwangju Republic of South Korea
- Aging Neuroscience Research Group, Korea Brain Research Institute Daegu Republic of South Korea
- Department of Biomedical Science, Chosun University Gwangju Republic of South Korea
| | - Jong‐In Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation Chungju Republic of South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation Chungju Republic of South Korea
- Department of Biomedical Engineering, Korea National University of Transportation Chungju Republic of South Korea
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Ho TKK, Kim M, Jeon Y, Na E, Ullah Z, Kim BC, Lee KH, Song J, Kim JG, Gwak J. Improving the multi‐class classification of Alzheimer’s disease with machine learning‐based techniques: An EEG‐fNIRS hybridization study. Alzheimers Dement 2021. [DOI: 10.1002/alz.057565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
| | - Minhee Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - YoungHoon Jeon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Eunchan Na
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School Gwangju Republic of South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center, Chosun University Gwangju Republic of South Korea
- Aging Neuroscience Research Group, Korea Brain Research Institute Daegu Republic of South Korea
- Department of Biomedical Science, Chosun University Gwangju Republic of South Korea
| | - Jong‐In Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Jae Gwan Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology Gwangju Republic of South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation Chungju Republic of South Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation Chungju Republic of South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation Chungju Republic of South Korea
- Department of Biomedical Engineering, Korea National University of Transportation Chungju Republic of South Korea
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Jeon Y, Ho TKK, Kang J, Kim BC, Lee KH, Song J, Gwak J. Machine learning–based detection model of early Alzheimer's disease using wearable device and gait assessment. Alzheimers Dement 2021. [DOI: 10.1002/alz.057563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- YoungHoon Jeon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju South Korea
| | - Thi Kieu Khanh Ho
- Department of Software, Korea National University of Transportation Chungju South Korea
| | - Jaeyong Kang
- Department of Software, Korea National University of Transportation Chungju South Korea
| | - Byeong C. Kim
- Department of Neurology, Chonnam National University Medical School Gwangju South Korea
| | - Kun Ho Lee
- Department of Biomedical Science, Chosun University Gwangju South Korea
- Aging Neuroscience Research Group, Korea Brain Research Institute Daegu South Korea
- Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center, Chosun University Gwangju South Korea
| | - Jong‐In Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology Gwangju South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation Chungju South Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation Chungju South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation Chungju South Korea
- Department of Biomedical Engineering, Korea National University of Transportation Chungju South Korea
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Fayaz M, Torokeldiev N, Turdumamatov S, Qureshi MS, Qureshi MB, Gwak J. An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network. Sensors (Basel) 2021; 21:s21227480. [PMID: 34833556 PMCID: PMC8619601 DOI: 10.3390/s21227480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 12/21/2022]
Abstract
In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications.
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Affiliation(s)
- Muhammad Fayaz
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Nurlan Torokeldiev
- Department of Mathematics and Natural Sciences, University of Central Asia, Khorog 736, Tajikistan;
| | - Samat Turdumamatov
- Department of Mathematics and Natural Sciences, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan;
| | - Muhammad Shuaib Qureshi
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Muhammad Bilal Qureshi
- Department of Computer Science and IT, University of Lakki Marwat, Lakki Marwat 28420, KPK, Pakistan;
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Kang J, Ullah Z, Gwak J. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. Sensors (Basel) 2021; 21:s21062222. [PMID: 33810176 PMCID: PMC8004778 DOI: 10.3390/s21062222] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022]
Abstract
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Affiliation(s)
- Jaeyong Kang
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT Convergence (Brain Korea PLUS 21), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Ahn J, Ho TKK, Kang J, Gwak J. Using Artificial Intelligence Methods for Dental Image Analysis: State-of-the-Art Reviews. j med imaging hlth inform 2020. [DOI: 10.1166/jmihi.2020.3254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
A large number of studies that use artificial intelligence (AI) methodologies to analyze medical imaging and support computer-aided diagnosis have been conducted in the biomedical engineering domain. Owing to the advances in dental diagnostic X-ray systems such as panoramic radiographs,
periapical radiographs, and dental computed tomography (CT), especially, dual-energy cone beam CT (CBCT), dental image analysis now presents more opportunities to discover new results and findings. Recent researches on dental image analysis have been increasingly incorporating analytics that
utilize AI methodologies that can be divided into conventional machine learning and deep learning approaches. This review first covers the theory on dual-energy CBCT and its applications in dentistry. Then, analytical methods for dental image analysis using conventional machine learning and
deep learning methods are described. We conclude by discussing the issues and suggesting directions for research in future.
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Nguyen T, Kim M, Gwak J, Lee JJ, Choi KY, Lee KH, Kim JG. Investigation of brain functional connectivity in patients with mild cognitive impairment: A functional near-infrared spectroscopy (fNIRS) study. J Biophotonics 2019; 12:e201800298. [PMID: 30963713 DOI: 10.1002/jbio.201800298] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 03/17/2019] [Accepted: 04/04/2019] [Indexed: 06/09/2023]
Abstract
This study examines brain functional connectivity in both cognitively normal seniors and patients with mild cognitive impairment (MCI) to elucidate prospective markers of MCI. A homemade four-channel functional near-infrared spectroscopy (fNIRS) system was employed to measure hemodynamic responses in the subjects' prefrontal cortex during a resting state, an oddball task, a 1-back task, and a verbal fluency task. Brain functional connectivity was calculated as the Pearson correlation coefficients between fNIRS channels. The results show that during the verbal fluency task, while the healthy control (HC) group presents a significantly stronger inter-hemispheric connectivity compared to intra-hemispheric connectivity, there is no difference between the inter- and intra-hemispheric connectivity in the MCI group. In addition, a comparison between the MCI and HC connectivity reveals that the MCI group has a statistically higher right and inter-hemispheric connectivity during the resting state, but a significantly lower left and inter-hemispheric connectivity during the verbal fluency test. These findings demonstrate the potential of fNIRS to study brain functional connectivity in neurodegenerative diseases.
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Affiliation(s)
- Thien Nguyen
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Minhee Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, Republic of Korea
| | - Jang J Lee
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Kyu Y Choi
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Kun H Lee
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- National Research Center for Dementia, Chosun University, Gwangju, Republic of Korea
| | - Jae G Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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Park CB, Jeong H, Choi KY, Kim BC, Lee JJ, Lee KH, Song JI, Gwak J. Gait pattern analysis to suggest one of factors classifying alzheimer's disease level using deep learning based on convolutional neural network. IBRO Rep 2019. [DOI: 10.1016/j.ibror.2019.07.1428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Kim M, Nguyen T, Kim BC, Gwak J, Lee JJ, Choi KY, Lee KH, Kim JG. Altered functional connectivity of prefrontal cortex in healthy elderly and Alzheimer's disease patient during a verbal fluency task: An fNIRS study. IBRO Rep 2019. [DOI: 10.1016/j.ibror.2019.07.1289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Gwak J, Khanh Ho TK, Park C, Choi KY, Kim BC, Song JI, Lee KH. P2-227: AD STAGE CLASSIFICATION USING INTEGRATED EEG-FNIRS FEATURES: MACHINE LEARNING APPROACHES. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.2634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jeonghwan Gwak
- Biomedical Research Institute and Department of Radiology; Seoul National University Hospital; Seoul Republic of South Korea
| | - Thi Kieu Khanh Ho
- Gwangju Institute of Science and Technology; Gwangju Republic of South Korea
| | - Cheolbin Park
- School of Electrical Engineering and Computer Science; Gwangju Institute of Science and Technology; Gwangju Republic of South Korea
| | - Kyu Yeong Choi
- National Research Center for Dementia; Chosun University; Gwangju Republic of South Korea
- Department of Premedics; Chosun University; Gwangju Republic of South Korea
| | - Byeong C. Kim
- National Research Center for Dementia; Chosun University; Gwangju Republic of South Korea
- Chonnam National University Medical School; Gwangju Republic of South Korea
- Department of Neurology; Chonnam National University Medical School; Gwangju Republic of South Korea
- National Research Centre for Dementia; Gwangju Republic of South Korea
| | - Jong-In Song
- School of Electrical Engineering and Computer Science; Gwangju Institute of Science and Technology; Gwangju Republic of South Korea
| | - Kun Ho Lee
- National Research Center for Dementia; Chosun University; Gwangju Republic of South Korea
- College of Natural Sciences; Chosun University; Gwangju Republic of South Korea
- Department of Life Sciences; Chosun University; Gwangju Republic of South Korea
- Department of Biomedical Science; Chosun University; Gwangju Republic of South Korea
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Gwak J, Lee SW, Jeon M, Song JI. P2‐272: Optimal Automatic Feature Selection through Iterative Reweighting for Class‐Imbalanced Data Sets to Classify Alzheimer's Disease/MCI/NC with High Diagnostic Accuracy. Alzheimers Dement 2016. [DOI: 10.1016/j.jalz.2016.06.1532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jeonghwan Gwak
- Gwangju Institute of Science and TechnologyGwangjuRepublic of Korea
| | | | - Moongu Jeon
- Gwangju Institute of Science and TechnologyGwangjuRepublic of Korea
| | - Jong-In Song
- Gwangju Institute of Science and TechnologyGwangjuRepublic of Korea
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Gwak J, Sim KM. An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies. APPL INTELL 2012. [DOI: 10.1007/s10489-012-0384-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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