| Recurrent residual U-Net for medical image segmentation MZ Alom, C Yakopcic, M Hasan, TM Taha, VK Asari Journal of medical imaging 6 (1), 014006-014006, 2019 | 2362* | 2019 |
| A state-of-the-art survey on deep learning theory and architectures MZ Alom, TM Taha, C Yakopcic, S Westberg, P Sidike, MS Nasrin, ... electronics 8 (3), 292, 2019 | 1850 | 2019 |
| The history began from alexnet: A comprehensive survey on deep learning approaches MZ Alom, TM Taha, C Yakopcic, S Westberg, P Sidike, MS Nasrin, ... arXiv preprint arXiv:1803.01164, 2018 | 1616 | 2018 |
| Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network MZ Alom, C Yakopcic, MS Nasrin, TM Taha, VK Asari Journal of digital imaging 32, 605-617, 2019 | 361 | 2019 |
| Intrusion detection using deep belief networks MZ Alom, VR Bontupalli, TM Taha 2015 National Aerospace and Electronics Conference (NAECON), 339-344, 2015 | 332 | 2015 |
| Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net) MZ Alom, C Yakopcic, TM Taha, VK Asari NAECON 2018-IEEE National Aerospace and Electronics Conference, 228-233, 2018 | 271 | 2018 |
| COVID_MTNet: COVID-19 detection with multi-task deep learning approaches MZ Alom, MM Rahman, MS Nasrin, TM Taha, VK Asari arXiv preprint arXiv:2004.03747, 2020 | 208 | 2020 |
| Improved inception-residual convolutional neural network for object recognition MZ Alom, M Hasan, C Yakopcic, TM Taha, VK Asari Neural Computing and Applications 32 (1), 279-293, 2020 | 149 | 2020 |
| Network intrusion detection for cyber security using unsupervised deep learning approaches MZ Alom, TM Taha 2017 IEEE national aerospace and electronics conference (NAECON), 63-69, 2017 | 149 | 2017 |
| Memristor crossbar deep network implementation based on a convolutional neural network C Yakopcic, MZ Alom, TM Taha 2016 International joint conference on neural networks (IJCNN), 963-970, 2016 | 135 | 2016 |
| Handwritten bangla digit recognition using deep learning MZ Alom, P Sidike, TM Taha, VK Asari arXiv preprint arXiv:1705.02680, 2017 | 128 | 2017 |
| Handwritten Bangla Character Recognition Using the State‐of‐the‐Art Deep Convolutional Neural Networks MZ Alom, P Sidike, M Hasan, TM Taha, VK Asari Computational intelligence and neuroscience 2018 (1), 6747098, 2018 | 126 | 2018 |
| Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv MZ Alom, M Hasan, C Yakopcic, TM Taha, VK Asari arXiv preprint arXiv:1802.06955 10, 2018 | 117 | 2018 |
| Extremely parallel memristor crossbar architecture for convolutional neural network implementation C Yakopcic, MZ Alom, TM Taha 2017 International Joint Conference on Neural Networks (IJCNN), 1696-1703, 2017 | 103 | 2017 |
| Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convolutional networks MZ Alom, T Aspiras, TM Taha, VK Asari arXiv preprint arXiv:1904.11126, 2019 | 60 | 2019 |
| Zahangir, et al." M Alom Inception Recurrent Convolutional Neural Network for Object Recognition …, 2019 | 51 | 2019 |
| Network intrusion detection for cyber security on neuromorphic computing system MZ Alom, TM Taha 2017 International Joint Conference on Neural Networks (IJCNN), 3830-3837, 2017 | 51 | 2017 |
| Medical image denoising with recurrent residual u-net (r2u-net) base auto-encoder S Nasrin, MZ Alom, R Burada, TM Taha, VK Asari 2019 IEEE national aerospace and electronics conference (NAECON), 345-350, 2019 | 49 | 2019 |
| Advanced deep convolutional neural network approaches for digital pathology image analysis: A comprehensive evaluation with different use cases MZ Alom, T Aspiras, TM Taha, VK Asari, TJ Bowen, D Billiter, S Arkell arXiv preprint arXiv:1904.09075, 2019 | 49 | 2019 |
| & Asari, VK (2019). A state-of-the-art survey on deep learning theory and architectures MZ Alom, TM Taha, C Yakopcic, S Westberg, P Sidike, MS Nasrin, ... Electronics 8 (3), 292, 0 | 49 | |
Tarek M. TahaElectrical and Computer Engineering, University of DaytonZweryfikowany adres z udayton.edu
