MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients

Deep Residual Learning for Image Recognition

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Dimensionality Reduction by Learning an Invariant Mapping

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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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Recent advances and clinical applications of deep learning in medical image analysis

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Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

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Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

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MetaMed: Few-shot medical image classification using gradient-based meta-learning

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An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19

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Meta-learning approaches for learning-to-learn in deep learning: A survey

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A smart ontology-based IoT framework for remote patient monitoring

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The Internet of Medical Things (IoMT): opportunities and challenges

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Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Hajamohideen, F., Shaffi, N., Mahmud, M., Karthikeyan, S., Sariri, A. A., Viswan, V., Abdesselam, A., & Initiative, A. D. N. (2023) · Brain Informatics · doi:10.1186/s40708-023-00184-w · Cited by 123

Popular deep learning algorithms for disease prediction: a review

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COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images

Muhammad, G., & Shamim Hossain, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.013 · Cited by 120

Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19

Li, J., Zhao, G., Tao, Y., Zhai, P., Chen, H., He, H., & Cai, T. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107848 · Cited by 104

Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review

Khan, J. I., Khan, J., Ali, F., Ullah, F., Bacha, J., & Lee, S. (2022) · IEEE Access · doi:10.1109/access.2022.3181605 · Cited by 96

Multimodal Triplet Attention Network for Brain Disease Diagnosis

Zhu, Q., Wang, H., Xu, B., Zhang, Z., Shao, W., & Zhang, D. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3199032 · Cited by 82

Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images

Malhotra, A., Mittal, S., Majumdar, P., Chhabra, S., Thakral, K., Mayank Vatsa, A., Singh, R., Chaudhury, S., Pudrod, A., & Agrawal, A. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108243 · Cited by 78

Uncertainty-driven ensembles of multi-scale deep architectures for image classification

Arco, J. E., Ortíz, A., Ramı́rez, J., Martínez-Murcia, F. J., Zhang, Y., & Górriz, J. M. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.010 · Cited by 71

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Hasan, M. M., Islam, M. U., Sadeq, M. J., Fung, W.-K., & Uddin, J. (2023) · Sensors · doi:10.3390/s23010527 · Cited by 70

Deep Learning Applied to Intracranial Hemorrhage Detection

Cortes-Ferre, L., Gutiérrez-Naranjo, M. Á., Egea-Guerrero, J. J., Pérez-Sánchez, S., & Balcerzyk, M. (2023) · Journal of Imaging · doi:10.3390/jimaging9020037 · Cited by 66

AI-Based human audio processing for COVID-19: A comprehensive overview

Deshpande, G., Batliner, A. M., & Schuller, B. W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108289 · Cited by 62

Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease

Gaur, L., Bhandari, M., Shikhar, B. S., Jhanjhi, N. Z., Shorfuzzaman, M., & Masud, M. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3527174 · Cited by 65

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

Oyelade, O. N., Ezugwu, A. E.-S., & Chiroma, H. (2021) · IEEE Access · doi:10.1109/access.2021.3083516 · Cited by 60

Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images

Ahmed, I., Chehri, A., & Jeon, G. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3294333 · Cited by 58

A Multimodal, Multimedia Point-of-Care Deep Learning Framework for COVID-19 Diagnosis

Rahman, M. A., Hossain, M. S., Alrajeh, N. A., & Gupta, B. B. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3421725 · Cited by 57

Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images

Sharma, A., & Mishra, P. K. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108826 · Cited by 56

Cross-task and cross-domain SAR target recognition: A meta-transfer learning approach

Zhang, Y., Guo, X., Leung, H., & Li, L. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109402 · Cited by 55

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Shankar, K., Perumal, E., Tiwari, P., Shorfuzzaman, M., & Gupta, D. K. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00800-x · Cited by 53

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh, A. H., Saba, A. I., Panchal, H. N., Shanmugan, S., Alsaleh, N. A., & Ahmadein, M. (2021) · Healthcare · doi:10.3390/healthcare9121614 · Cited by 53

Multimodal sensor fusion framework for residential building occupancy detection

Tan, S. Y., Jacoby, M., Saha, H., Florita, A. R., Henze, G. P., & Sarkar, S. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111828 · Cited by 62

Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

Ahmed, I., Ahmad, M., & Jeon, G. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.03.002 · Cited by 51

IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach

Mukherjee, R., Kundu, A., Mukherjee, I., Gupta, D. K., Tiwari, P., Khanna, A. K., & Shorfuzzaman, M. (2021) · Computing · doi:10.1007/s00607-021-00951-9 · Cited by 48

uTHCD: A New Benchmarking for Tamil Handwritten OCR

Shaffi, N., & Hajamohideen, F. (2021) · IEEE Access · doi:10.1109/access.2021.3096823 · Cited by 55

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning

Madhavan, M. V., Khamparia, A., Gupta, D. K., Pande, S. D., Tiwari, P., & Hossain, M. S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06171-8 · Cited by 49

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

Shorfuzzaman, M., Masud, M., Alhumyani, H. A., Anand, D., & Singh, A. D. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/5513679 · Cited by 51

COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach

Jadon, S. (2021) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2581496 · Cited by 48

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer’s Disease

Shaffi, N., Karthikeyan, S., Viswan, V., Hajamohideen, F., Abdesselam, A., & Mahmud, M. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500291 · Cited by 47

DSRD-Net: Dual-stream residual dense network for semantic segmentation of instruments in robot-assisted surgery

Mahmood, T., Cho, S. W., & Park, K. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117420 · Cited by 39

Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey

Özen, F. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25746 · Cited by 38

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

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Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks

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ANN Assisted-IoT Enabled COVID-19 Patient Monitoring

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Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images

Brima, Y., Atemkeng, M., Djiokap, S. R. T., Ebiele, M., & Tchakounté, F. (2021) · Diagnostics · doi:10.3390/diagnostics11081480 · Cited by 37

FCSCNN: Feature centralized Siamese CNN-based android malware identification

Kong, K., Zhang, Z., Yang, Z., & Zhang, Z. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102514 · Cited by 37

Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning

Meng, Y., Bridge, J., Addison, C. A., Wang, M., Merritt, C., Franks, S., Mackey, M., Messenger, S., Sun, R., FitzMaurice, T. S., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102722 · Cited by 35

Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study

Nishio, M., Kobayashi, D., Nishioka, E., Matsuo, H., Urase, Y., Onoue, K., Ishikura, R., Kitamura, Y., Sakai, E., Tomita, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-11990-3 · Cited by 35

An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals

Zhou, Z., Asghar, M. A., Nazir, D., Siddique, K., Shorfuzzaman, M., & Mehmood, R. M. (2022) · Cluster Computing · doi:10.1007/s10586-022-03705-0 · Cited by 35

Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease

Shaffi, N., Hajamohideen, F., Mahmud, M., Abdesselam, A., Karthikeyan, S., & Sariri, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15037-1_23 · Cited by 34

Technology‐driven 5G enabled e‐healthcare system during COVID‐19 pandemic

Alshammari, N. O., Sarker, M. N. I., Kamruzzaman, M. M., Alruwaili, M., Alanazi, S., Raihan, M. L., & AlQahtani, S. A. (2021) · IET Communications · doi:10.1049/cmu2.12240 · Cited by 34

COV-ELM classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images

Rajpal, S., Agarwal, M., Rajpal, A., Lakhyani, N., Saggar, A., & Kumar, N., V (2022) · Intelligent Decision Technologies · doi:10.3233/idt-210055 · Cited by 34

Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays

Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., & Soda, P. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108242 · Cited by 33

Enhancing concrete defect segmentation using multimodal data and Siamese Neural Networks

Pozzer, S., Ramos, G., Azar, E. R., Osman, A., Refai, A. E., López, F., Ibarra‐Castanedo, C., & Maldague, X. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105594 · Cited by 31

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment

Bao, G., Chen, H., Liu, T., Gong, G., Yin, Y., Wang, L., & Wang, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108499 · Cited by 31

Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease

Shaffi, N., Hajamohideen, F., Abdesselam, A., Mahmud, M., & Karthikeyan, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24801-6_16 · Cited by 31

A Sustainable Deep Learning-Based Framework for Automated Segmentation of COVID-19 Infected Regions: Using U-Net with an Attention Mechanism and Boundary Loss Function

Ahmed, I., Chehri, A., & Jeon, G. (2022) · Electronics · doi:10.3390/electronics11152296 · Cited by 31

Pandemic Management for Diseases Similar to COVID-19 Using Deep Learning and 5G Communications

Muhammad, G., Alqahtani, S., & Alelaiwi, A. (2021) · IEEE Network · doi:10.1109/mnet.011.2000739 · Cited by 29

Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms

Bai, J., Jin, A., Wang, T., Yang, C., & Nabavi, S. (2022) · Medical Physics · doi:10.1002/mp.15598 · Cited by 31

Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images

Imani, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102602 · Cited by 29

BLNN: Multiscale Feature Fusion‐Based Bilinear Fine‐Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

Gao, M., Wang, F., Song, P., Liu, J., & Qi, D. (2021) · Journal of Sensors · doi:10.1155/2021/8109496 · Cited by 29

An artificial intelligence-driven image quality assessment system for whole-body [18F]FDG PET/CT

Qi, C., Wang, S., Yu, H., Zhang, Y., Hu, P., Tan, H., Shi, Y., & Shi, H. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-06078-z · Cited by 29

Deep learning in multimedia healthcare applications: a review

Tobón, D. P., Hossain, M. S., Muhammad, G., Bilbao, J., & Saddik, A. E. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00948-0 · Cited by 29

Meta-learning in Healthcare: A Survey

Rafiei, A., Moore, R., Jahromi, S., Hajati, F., & Kamaleswaran, R. (2024) · SN Computer Science · doi:10.1007/s42979-024-03166-9 · Cited by 33

Deep Learning in Medical Image Analysis: A Comprehensive Review of Algorithms, Trends, Applications, and Challenges

Lepcha, D. C., Goyal, B., Dogra, A., Alkhayyat, A. H., Sahu, P. K., Ali, A., & Kukreja, V. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.070964 · Cited by 29

Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging

Liang, G., Greenwell, C., Zhang, Y., Xing, X., Wang, X., Kavuluru, R., & Jacobs, N. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3110805 · Cited by 26

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures

Zeng, B., Wang, H., Xu, J., Tu, P., Joskowicz, L., & Chen, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3264298 · Cited by 25

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray

Wang, Y., Jiang, C., Wu, Y., Lv, T., Sun, H., Liu, Y., Li, L., & Pan, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3205167 · Cited by 24

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Hong, Q., Lin, L., Li, Z., Li, Q., Yao, J., Wu, Q., Liu, K., & Tian, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3280646 · Cited by 25

New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

Karim, A., Kaya, H., Alcan, V., Şen, B., & Hadımlıoğlu, İ. A. (2022) · Symmetry · doi:10.3390/sym14051003 · Cited by 23

Detection of COVID‐19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network

Foysal, M., Hossain, A. B. M. A., Yassine, A., & Hossain, M. S. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/4301745 · Cited by 23

IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans

Shorfuzzaman, M. (2021) · Computing · doi:10.1007/s00607-021-00971-5 · Cited by 23

A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images

Masud, M. (2022) · Multimedia Systems · doi:10.1007/s00530-021-00857-8 · Cited by 24

The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer

García‐Barberán, V., del Pulgar, M. E. G., Guamán, H. M., & Benito‐Martín, A. (2025) · Extracellular Vesicles and Circulating Nucleic Acids · doi:10.20517/evcna.2024.51 · Cited by 22

A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces

Liu, H., Wang, C., Jiang, X., & Khishe, M. (2023) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2024-0005 · Cited by 22

Periphery-aware COVID-19 diagnosis with contrastive representation enhancement

Hou, J., Xu, J., Jiang, L., Du, S., Feng, R., Zhang, Y., Shan, F., & Xue, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108005 · Cited by 22

Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning

Song, Q., Zheng, Y., Yang, J. J., Huang, Y., Sheng, W., & Chen, S. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3164412 · Cited by 22

The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions

Novakovic, A., & Marshall, A. H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108790 · Cited by 23

STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic

Ghosh, S., & Mukherjee, A. (2022) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-022-00458-2 · Cited by 21

Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-Ray Images

Satu, M. S., Ahammed, K., Abedin, M. Z., Rahman, M. A., Islam, S. M. S., Azad, A., Alyami, S. A., & Moni, M. A. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34619-4_13 · Cited by 21

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Shah, H., Shah, S., Tanwar, S., Gupta, R., & Kumar, N. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00818-1 · Cited by 22

Lightweight Fault Diagnosis via Siamese Network for Few-Shot EHA Circuit Analysis

Jia, Z. Y., Liu, Z., Li, Z., Wang, K., & Vong, C. (2025) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2025.3589534 · Cited by 20

Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks

Shen, J., Liu, F., Xu, M., Fu, L., Dong, Z., & Wu, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116547 · Cited by 20

Few-shot contrastive learning for image classification and its application to insulator identification

Li, L., Jin, W., & Huang, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02769-6 · Cited by 20

CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19

Ornob, T. R., Roy, G., & Hassan, E. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101156 · Cited by 21

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

Jwo, J., Lin, C.-S., Lee, C., Zhang, L., & Huang, S. (2021) · Applied Sciences · doi:10.3390/app11178243 · Cited by 19

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in $$[^{18}$$F]FDG PET/CT

Nikulin, P., Zschaeck, S., Maus, J., Cegła, P., Lombardo, E., Furth, C., Kaźmierska, J., Rogasch, J. M. M., Holzgreve, A., Albert, N. L., et al. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06197-1 · Cited by 19

A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy

Masud, M., Alhamid, M. F., & Zhang⋆, Y. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3470976 · Cited by 19

Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation

Dong, A., Liu, J., Zhang, G., Wei, Z., Zhai, Y., & Lv, G. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109732 · Cited by 18

Detection of Multiple Respiration Patterns Based on 1D SNN from Continuous Human Breathing Signals and the Range Classification Method for Each Respiration Pattern

Hong, J., Kim, S.-H., & Han, G.-T. (2023) · Sensors · doi:10.3390/s23115275 · Cited by 18

Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study

Galán-Cuenca, A., Gallego, A., Saval-Calvo, M., & Pertusa, A. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01285-w · Cited by 19

In-Home Gait Abnormality Detection Through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive Learning

Dong, Y., Kim, S. E., Schadl, K., Huang, P., Ding, W., Rose, J., & Noh, H. Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3413815 · Cited by 17

Optimal Diagnosis of COVID‐19 Based on Convolutional Neural Network and Red Fox Optimization Algorithm

Khorami, E., Babaei, F., & Azadeh, A. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4454507 · Cited by 16

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study

Ligi, S. V., Kundu, S. S., Kumar, R., Narayanamoorthi, R., Lai, K. W., & Dhanalakshmi, S. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/5998042 · Cited by 16

Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

Khan, A. A., Asim, W., Ulhaq, A., Ghazi, B. A., & Robinson, R. W. (2021) · Remote Sensing · doi:10.3390/rs13112194 · Cited by 16

COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound

Song, J., Ebadi, A., Florea, A., Xi, P., Tremblay, S., & Wong, A. M. (2023) · Sensors · doi:10.3390/s23052621 · Cited by 16

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module

Liu, Y., Xing, W., Zhao, M., & Lin, M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08604-y · Cited by 15

Affective Interaction: Attentive Representation Learning for Multi-Modal Sentiment Classification

Zhang, Y., Tiwari, P., Rong, L., Chen, R., Alnajem, N. A., & Hossain, M. S. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3527175 · Cited by 15

DFU-Helper: An Innovative Framework for Longitudinal Diabetic Foot Ulcer Diseases Evaluation Using Deep Learning

Toofanee, M. S. A., Dowlut, S., Hamroun, M., Tamine, K., Duong, A. K., Petit, V., & Sauveron, D. (2023) · Applied Sciences · doi:10.3390/app131810310 · Cited by 15

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Huang, Z., Zhou, Q., Zhu, X., & Zhang, X. (2021) · Sensors · doi:10.3390/s21030764 · Cited by 15

Face recognition based on Siamese convolutional neural network using Kivy framework

Aufar, Y., & Sitanggang, I. S. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i2.pp764-772 · Cited by 14

COVID-Net FewSE: An Open-Source Deep Siamese Convolutional Network Model for Few-Shot Detection of COVID-19 Infection from X-Ray Images

Ebadi, A., Azimi, H., Xi, P., Tremblay, S., & Wong, A. (2022) · Journal of Computational Vision and Imaging Systems · doi:10.15353/jcvis.v7i1.4891

Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images

Mohit, K., Gupta, R. K., & Kumar, B. (2022) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon56432.2022.9986406 · Cited by 4

A Siamese neural network-based diagnosis of COVID-19 using chest X-rays

Taş, E., & Atli, A. H. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10326-8 · Cited by 1

Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19

Li, J., Zhao, G., Tao, Y., Zhai, P., Chen, H., He, H., & Cai, T. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107848 · Cited by 104

Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images

Chen, X., Yao, L., Zhou, T., Dong, J., & Zhang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107826 · Cited by 191