| Prediction and classification of Alzheimer’s disease based on combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers Y Gupta, RK Lama, GR Kwon, ... Frontiers in computational neuroscience 13, 72, 2019 | 154 | 2019 |
| Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases S Ahmed, KY Choi, JJ Lee, BC Kim, GR Kwon, KH Lee, HY Jung IEEE Access 7, 73373-73383, 2019 | 128 | 2019 |
| Diagnosis of Alzheimer’s Disease Using Dual‐Tree Complex Wavelet Transform, PCA, and Feed‐Forward Neural Network D Jha, JI Kim, GR Kwon Journal of healthcare engineering 2017 (1), 9060124, 2017 | 126 | 2017 |
| Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images Y Gupta, KH Lee, KY Choi, JJ Lee, BC Kim, GR Kwon, ... PLoS One 14 (10), e0222446, 2019 | 94 | 2019 |
| Beacon based indoor positioning system using weighted centroid localization approach S Subedi, GR Kwon, S Shin, S Hwang, JY Pyun 2016 Eighth International Conference on Ubiquitous and Future Networks …, 2016 | 94 | 2016 |
| Alzheimer disease classification using KPCA, LDA, and multi‐kernel learning SVM S Alam, GR Kwon, Alzheimer's Disease Neuroimaging Initiative International Journal of Imaging Systems and Technology 27 (2), 133-143, 2017 | 90 | 2017 |
| Twin SVM‐Based Classification of Alzheimer’s Disease Using Complex Dual‐Tree Wavelet Principal Coefficients and LDA S Alam, GR Kwon, JI Kim, CS Park Journal of healthcare engineering 2017 (1), 8750506, 2017 | 82 | 2017 |
| Alzheimer’s disease classification from brain MRI based on transfer learning from CNN B Khagi, CG Lee, GR Kwon 2018 11th biomedical engineering international conference (BMEiCON), 1-4, 2018 | 81 | 2018 |
| 3D CNN design for the classification of Alzheimer’s disease using brain MRI and PET B Khagi, GR Kwon IEEE Access 8, 217830-217847, 2020 | 78 | 2020 |
| Comparative analysis of Alzheimer's disease classification by CDR level using CNN, feature selection, and machine‐learning techniques B Khagi, GR Kwon, R Lama International Journal of Imaging Systems and Technology 29 (3), 297-310, 2019 | 73 | 2019 |
| Performance of classification based on PCA, linear SVM, and Multi-kernel SVM S Alam, M Kang, JY Pyun, GR Kwon 2016 Eighth International Conference on Ubiquitous and Future Networks …, 2016 | 67 | 2016 |
| Level set method with automatic selective local statistics for brain tumor segmentation in MR images K Thapaliya, JY Pyun, CS Park, GR Kwon Computerized Medical Imaging and Graphics 37 (7-8), 522-537, 2013 | 63 | 2013 |
| Alzheimer’s disease diagnosis and biomarker analysis using resting-state functional MRI functional brain network with multi-measures features and hippocampal subfield and … U Khatri, GR Kwon Frontiers in aging neuroscience 14, 818871, 2022 | 58 | 2022 |
| An efficient deep neural network binary classifier for Alzheimer’s disease classification R Prajapati, U Khatri, GR Kwon 2021 international conference on artificial intelligence in information and …, 2021 | 58 | 2021 |
| Hybrid image-retrieval method for image-splicing validation NT Pham, JW Lee, GR Kwon, CS Park Symmetry 11 (1), 83, 2019 | 56 | 2019 |
| Automated detection of Alzheimer’s disease and mild cognitive impairment using whole brain MRI FUR Faisal, GR Kwon IEEE Access 10, 65055-65066, 2022 | 53 | 2022 |
| Pixel‐Label‐Based Segmentation of Cross‐Sectional Brain MRI Using Simplified SegNet Architecture‐Based CNN B Khagi, GR Kwon Journal of healthcare engineering 2018 (1), 3640705, 2018 | 53 | 2018 |
| Alzheimer's disease detection using extreme learning machine, complex dual tree wavelet principal coefficients and linear discriminant analysis D Jha, S Alam, JY Pyun, KH Lee, GR Kwon Journal of Medical Imaging and Health Informatics 8 (5), 881-890, 2018 | 50 | 2018 |
| CNN Models Performance Analysis on MRI images of OASIS dataset for distinction between Healthy and Alzheimer's patient B Khagi, B Lee, JY Pyun, GR Kwon 2019 International Conference on Electronics, Information, and Communication …, 2019 | 45 | 2019 |
| Classification and graphical analysis of Alzheimer’s disease and its prodromal stage using multimodal features from structural, diffusion, and functional neuroimaging data and … Y Gupta, JI Kim, BC Kim, GR Kwon Frontiers in aging neuroscience 12, 238, 2020 | 44 | 2020 |
