| MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey N Yamanakkanavar, JY Choi, B Lee Sensors 20 (11), 3243, 2020 | 270 | 2020 |
| Automatic Segmentation of Brain MRI using a Novel Patch-wise U-net Deep Architecture B Lee, N Yamanakkanavar, JY Choi PLoS ONE 15 (8), e0236493, 2020 | 117 | 2020 |
| A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI N Yamanakkanavar, B Lee Computers in Biology and Medicine 136, 104761, 2021 | 61 | 2021 |
| Using a patch-wise M-net convolutional neural network for tissue segmentation in brain MRI images N Yamanakkanavar, B Lee IEEE Access 8, 120946-120958, 2020 | 38 | 2020 |
| MF2-Net: A multipath feature fusion network for medical image segmentation N Yamanakkanavar, B Lee Engineering Applications of Artificial Intelligence 114, 105004, 2022 | 32 | 2022 |
| Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique Y Nagaraj, P Madipalli, J Rajan, PK Kumar, AV Narasimhadhan Biomedical Signal Processing and Control 40, 462-472, 2018 | 29 | 2018 |
| AMCC-Net: An asymmetric multi-cross convolution for skin lesion segmentation on dermoscopic images C Dayananda, N Yamanakkanavar, T Nguyen, B Lee Engineering Applications of Artificial Intelligence 122, 1-12, 2023 | 27 | 2023 |
| Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine Y Nagaraj, AHS Teja, AV Narasimhadhan Arabian Journal for Science and Engineering 44 (4), 3489-3496, 2019 | 21 | 2019 |
| SM-SegNet: a lightweight squeeze M-SegNet for tissue segmentation in brain MRI scans N Yamanakkanavar, JY Choi, B Lee Sensors 22 (14), 5148, 2022 | 15 | 2022 |
| Carotid wall segmentation in longitudinal ultrasound images using structured random forest Y Nagaraj, CS Asha, AV Narasimhadhan Computers & Electrical Engineering 69, 753-767, 2018 | 14 | 2018 |
| Assessment of speckle denoising in ultrasound carotid images using least square bayesian estimation approach Y Nagaraj, CS Asha, AV Narasimhadhan 2016 IEEE Region 10 Conference (TENCON), 1001-1004, 2016 | 10 | 2016 |
| Multiscale and hierarchical feature-aggregation network for segmenting medical images N Yamanakkanavar, JY Choi, B Lee Sensors 22 (9), 3440, 2022 | 8 | 2022 |
| Automatic Segmentation of Intima Media Complex in Common Carotid Artery using Adaptive Wind Driven Optimization P Madipalli, S Kotta, H Dadi, Y Nagaraj, CS Asha, AV Narasimhadhan 2018 Twenty Fourth National Conference on Communications (NCC), 1-6, 2018 | 8 | 2018 |
| Brain tissue segmentation using patch-wise m-net convolutional neural network N Yamanakkanavar, B Lee 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 1-4, 2020 | 4 | 2020 |
| Comparison of edge detection algorithms in the framework of despeckling carotid ultrasound images based on bayesian estimation approach Y Nagaraj, AV Narasimhadhan National Conference on Computer Vision, Pattern Recognition, Image …, 2017 | 3 | 2017 |
| A No-reference Image Quality Assessment based on Reference Generating Network S Ghimire, N Yamanakkanavar, B Lee 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 1-4, 2020 | 1 | 2020 |
| A Real-Time Gesture Controlled Robot Car A Nair, S Rajole, U Patil, R Jayaramu, N Yamanakkanavar International Conference on Computing, Communication, Security and …, 2024 | | 2024 |
| A squeeze M-SegNet architecture for segmentation of brain tissues on MRI N Yamanakkanavar, B Lee 한국통신학회 학술대회논문집, 539-542, 2021 | | 2021 |
| M-SegNet: MRI 에서 뇌 조직을 분할하기 위한 CNN 아키텍처 이범식 한국차세대컴퓨팅학회 학술대회, 409-411, 2021 | | 2021 |
| Squeeze U-SegNet: A CNN architecture for Automatic segmentation of brain MRI C Dayananda, N Yamanakkanavar, B Lee The 9th International Conference on Smart Media and Applications (SMA 2020), 2020 | | 2020 |
Bumshik Lee (이범식)Korea Institute of Energy Technology (KENTECH)Verified email at kentech.ac.kr
