Cited by 327COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingAdvanced Neural Network Applications

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Open access

Toraman, Suat, Alakus, Talha Burak, Turkoglu, Ibrahim·2020·Chaos, Solitons & Fractals

DOI 10.1016/j.chaos.2020.110122PMC PMC7357532

Open this paper in CoChat — free account

Abstract

Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.chaos.2020.110122

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,652
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338
Coronavirus Disease 2019 (COVID-19): A Perspective from China
Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020) · Radiology · doi:10.1148/radiol.2020200490 · Cited by 2,070
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054
Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy
Grasselli, G., Pesenti, A., & Cecconi, M. (2020) · JAMA · doi:10.1001/jama.2020.4031 · Cited by 2,019

Show all 60 references

Research works printed but not linked: 1

Rajput, K.S., Wibowo, S., Hao, C., and Majmudar, M.On arrhythmia detection by deep learning and multidimensional representation, arXiv, 190400138, 2019.

Sorted by citations

Items 201–300 of 324

Two Advanced Models of the Function of MRT Public Transportation in Taipei
Chen, Y., Lin, C.-K., Chen, S., & Chen, S.-H. (2021) · Electronics · doi:10.3390/electronics10091048 · Cited by 4
A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19
Reda, A. M., Barakat, S., & Rezk, A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019809 · Cited by 5
COVID-19 Detection from X-Ray Images using Convoluted Neural Networks: A Literature Review
Alrusaini, O. A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130313 · Cited by 5
Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans
Smadi, A. A., Abugabah, A., Mahenge, S. F., & Shahid, F. (2022) · International Computer Science and Engineering Conference · doi:10.1109/icsec56337.2022.10049310 · Cited by 4
A novel framework based on deep learning for COVID-19 diagnosis from X-ray images
Moghaddam, S. M. J. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1375 · Cited by 4
A Federated Multi-stage Light-Weight Vision Transformer for Respiratory Disease Detection
Sahoo, P., Sharma, S. K., Saha, S., & Mondal, S. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8141-0_23 · Cited by 4
A Graph-based Methodology for Tracking Covid-19 in Time Series Datasets
Mouden, Z. A. E., Jakimi, A., Taj, R. M., & Hajar, M. (2020) · IEEE International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) · doi:10.1109/icecocs50124.2020.9314516 · Cited by 4
Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods
Zgheib, R., Kamalov, F., Chahbandarian, G., & Labban, O. E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84529-2_52 · Cited by 4
A novel study for automatic two-class COVID-19 diagnosis (between COVID-19 and Healthy, Pneumonia) on X-ray images using texture analysis and 2-D/3-D convolutional neural networks
Yaşar, H., & Ceylan, M. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00892-z · Cited by 4
Exploring the potential of artificial intelligence and machine learning in healthcare: challenges and research directions
Manimaran, S., Priya, D. U., Maria, A., & Rajasekaran, A. S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05372-3 · Cited by 4
An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification
Sayeed, A., Khansur, N. O., Srizon, A. Y., Faruk, M. F., Alyami, S. A., Azad, A., & Moni, M. A. (2024) · IET Image Processing · doi:10.1049/ipr2.13106 · Cited by 3
Deep Neural Networks for Skin Cancer Classification: Analysis of Melanoma Cancer Data
Afrifa, S., Varadarajan, V., Appiahene, P., Zhang, T., Gyamfi, D., & Gyening, R.-M. O. M. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.1.1-11 · Cited by 3
An Efficient Deep Learning Framework using CapsNet and SOM for Multidrug-Resistant Tuberculosis Detection and Analysis in CXR Images
Sharmila, V. C., Remya, K. R., & Sam Praveen, P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.583 · Cited by 3
Automated COVID-19 detection using Deep Convolutional Neural Network and Chest X-ray Images
Agrawal, T., & Choudhary, P. (2021) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe53109.2021.9751799 · Cited by 3
New Coronavirus 2 (SARS-CoV-2) Detection Method from Human Nucleic Acid Sequences Using Capsule Networks
Daş, B., & Toraman, S. (2023) · Brazilian Archives of Biology and Technology · doi:10.1590/1678-4324-2023220316 · Cited by 3
Self-citation
Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images
Escorcia‐Gutierrez, J., Gamarra, M., Soto-Díaz, R., Alsafari, S., Yafoz, A., & Mansour, R. F. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033731 · Cited by 3
Polynomial Regression Model to Predict Future Covid Cases
Suriya, S., & Krishna, R. S. (2023) · Journal of Artificial Intelligence and Capsule Networks · doi:10.36548/jaicn.2023.2.004 · Cited by 3
COVID ‐19: A systematic review of prediction and classification techniques
Varma, O. R., Kalra, M., & Kirmani, S. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22905 · Cited by 3
Transfer learning with fine-tuned deep CNN model for COVID-19 diagnosis from chest X-ray images
(2023) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2022.10100044 · Cited by 4
Development of abnormal facial temperature detection technology using thermal imaging to prevent the spread of infectious diseases
Hong, H. G., Jang, S. H., Yun, H. Y., & Cho, Y. J. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101754 · Cited by 3
Detection of Thoracic Diseases Using Chest X-Ray: A Comparative Study of Binary Class and Multiclass Classification Using Deep Learning
Agarwal, A. K., Vysakh, L., Paduri, A. R., Mabiyan, R., Wattamwar, M. S., Divya, L., & Darapaneni, N. (2023) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon58714.2023.10450111 · Cited by 3
Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach
Rajendran, T., Hanumanthakari, S., Sekar, S., & Kirubakaran, S. (2024) · Network Computation in Neural Systems · doi:10.1080/0954898x.2024.2350579 · Cited by 3
A Robust Technique for Detecting SARS-CoV-2 from X-Ray Image using 2D Convolutional Neural Network and Particle Swarm Optimization
Asghar, M. A., Razzaq, S., Rasheed, S., & Fawad, F. (2020) · International Conference on Open Source Systems and Technologies (ICOSST) · doi:10.1109/icosst51357.2020.9333084 · Cited by 3
Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network
Lotfy, M., El-Bakry, H. M., El-Gayar, M. M., El–Sappagh, S., I, G. A. M., Soliman, A. A., & Kwak, K. S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.024193 · Cited by 3
CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray ImagesPreprint
Khan, M. S. I., Rahman, A., Karim, M. R., Bithi, N. I., Band, S. S., Dehzangi, A., & Alinejad-Rokny, H. (2021) · medRxiv · doi:10.1101/2021.05.19.21257430 · Cited by 3
Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images
Salamon, S., & Książek, W. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207212 · Cited by 2
Development of a predictive classification model for determining the category of fatal mine accidents using text mining techniques
Kumar, A., & Gupta, S. (2025) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2025.2576344 · Cited by 2
Identification of COVID-19 and Pneumonia in X-Ray Images using Transfer Learning
Devi, O. R., Mayee, M. K., & Aarathi, S. (2022) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei53556.2022.9776801 · Cited by 2
Predicting COVID-19 Cases on a Large Chest X-Ray Dataset Using Modified Pre-trained CNN Architectures
Karac, A. (2023) · Applied Computer Systems · doi:10.2478/acss-2023-0005 · Cited by 3
A Study on Food Classification and its Significance in Promoting Tourism and Boosting the Economy
Tiwari, R. G., Gautam, V., Trivedi, N. K., Jain, A. K., & Sharma, V. (2023) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss58487.2023.10250473 · Cited by 2
Modified Local Gradient Coding Pattern (MLGCP): A Handcrafted Feature Descriptor for Classification of Infectious Diseases
Bondugula, R. K., & Udgata, S. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6553-3_36 · Cited by 2
3Cs: Unleashing Capsule Networks for Robust COVID-19 Detection Using CT Images
Alaufi, R., Abukhodair, F., & Kalkatawi, M. (2024) · COVID · doi:10.3390/covid4080077 · Cited by 2
Classification of COVID–19 and Pneumonia X–ray Images Using a Transfer Learning Approach
R, S. K. H., Bhargavi, M. S., & Kumar, C. P. (2021) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp52854.2021.9550878 · Cited by 3
A Survey of Deep Learning on COVID-19 Identification Through X-Ray Images
Novamizanti, L., & Rajab, T. L. E. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-1804-9_4 · Cited by 2
Detection of COVID-19 from X-Ray Images Using Transfer Learning Neural Networks
Majeed, S. A., Darghaoth, A. M. H., Hamed, N. M. Z., Yahya, Y. A., Raed, S., & Dawood, Y. S. (2021) · Information Technology To Enhance e-learning and Other Application (IT-ELA) · doi:10.1109/it-ela52201.2021.9773657 · Cited by 2
Machine Learning and Deep Learning-Based Detection and Analysis of COVID-19 in Chest X-Ray Images
Kumar, K. K., Shokeen, H., Gambhir, S., Kumar, A., & Saraswat, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-3679-1_12 · Cited by 2
Neural Networks for the Diagnosis of Covid-19 in Chest X-ray Images: A Systematic Review and Meta-Analysis
Andrade-Girón, D., Rodriguez, W. J. M., Lioo-Jordán, F. D. M., Cadenas, G. J. V., & de Salinas, F. D. M. G. T. (2023) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.9.4212 · Cited by 2
COVID-19, Pneumonia, Tuberculosis Classification Using Chest X-Ray Images
Hashmi, M. F., Mathur, P., & Keskar, A. G. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1463-0.ch003 · Cited by 2
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention NetworkPreprint
Zhang, K., Karanth, S. S., Patel, B., Murphy, R., & Jiang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01147 · Cited by 2
An Efficient Computational Numerical Approach For Nonlinear Mathematical Influenza Disease ModellingPreprint
Sabir, Z., Raja, M. A. Z., Ali, M. R., Arbi, A., & Kristiawan, M. (2021) · Research Square · doi:10.21203/rs.3.rs-173611/v1 · Cited by 2
Deep Learning Applied to COVID-19 Detection in X-Ray Images
Arteaga-Arteaga, H. B., delaPava, M., Mora-Rubio, A., Bravo-Ortíz, M. A., Alzate-Grisales, J. A., Arias-Garzón, D., López-Murillo, L. H., Buitrago-Carmona, F., Villa-Pulgarín, J. P., Mercado-Ruiz, E., et al. (2022) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-6684-2304-2.ch007 · Cited by 2
Sparse-Attention Based Deep Convolutional Neural Network for Pneumonia Detection
Waris, S. F., Karthikeya, V. N. R., & Supraja, M. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205380 · Cited by 1
A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices
Mali, D., & Patil, S. A. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31528 · Cited by 1
Dempster-shafer deep capsule attention model (DDCAM)
Mehravaran, Z., Ghanizadeh, A. N., Hamidzadeh, J., & Harati, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20866-3 · Cited by 1
A Comparative Study of Transfer Learning Approaches For Medical Image Analysis
Malla, P. P., & Sahu, S. (2024) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc64653.2024.10984272 · Cited by 1
Multi-Label Chest X-Ray Classification with CNNs, Transformers, and Grad-CAM: An Explainable AI Approach
Bamatraf, M., Alsolami, F., & Saleh, M. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425400087 · Cited by 1
Trans-CAMNet: A Transformer-Based Grad-CAM Network for Lung Disease Classification
Kansal, K., Singh, A., Singh, K. K., & Kansal, K. (2025) · International Workshop on Computer Modeling and Intelligent Systems (CMIS) · doi:10.55056/ceur-ws.org/vol-3988/paper25.pdf · Cited by 1
Prediction of human protein functions with protein mapping techniques and deep learning model
Alakuş, T. B., & Türkoğlu, İ. (2022) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2021.51261 · Cited by 1
One-class Classification for Identifying COVID-19 in X-Ray Images
Pérez‐Careta, E., Hernández-Farías, D. I., Guzman-Sepulveda, J. R., Cisneros, M. T., Fraga, T. C., Martínez-Espinosa, J. C., & Guzmán-Cabrera, R. (2022) · Programming and Computer Software · doi:10.1134/s0361768822040041 · Cited by 1
Web Application Based on Deep Learning for Detecting COVID-19 Using Chest X-Ray Images
Al-madani, A. M., Gaikwad, A. T., Ahmed, Z. A. T., Mahale, V. H., Alsubari, S. N., & Tawfik, M. (2022) · TELe-Health · doi:10.1007/978-3-030-99457-0_18 · Cited by 1
COVID‐19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence
Abdulaal, M. J., Mehedi, I. M., Abusorrah, A. M., Aljohani, A. J., Milyani, A. H., Rana, M. M., & Mahmoud, M. M. E. A. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/5297709 · Cited by 1
Retracted
A CNN for Covid Detection Using ECG signals
Muscato, F., Corino, V., Rivolta, M. W., Cerveri, P., Sanzo, A., Vicentini, A., Sassi, R., & Mainardi", L. (2022) · Computing in cardiology · doi:10.22489/cinc.2022.196 · Cited by 1
Derin Öğrenme ile Göğüs Röntgeni Görüntülerinden COVID-19 ve Viral Pnömoni Tespiti
Tüfekçi, P., & GEZİCİ, B. (2023) · Afyon Kocatepe University Journal of Sciences and Engineering · doi:10.35414/akufemubid.1152432 · Cited by 1
COVID-19 Detection using Curvelet Transformation and Support Vector Machine
Yaqoob, S., Akram, A., Mansoor, T., & Mustafa, H. (2023) · Journal of Innovative Computing and Emerging Technologies · doi:10.56536/jicet.v3i1.55 · Cited by 1
Deep Learning for Combating COVID-19 Pandemic in Internet of Medical Things (IoMT) Networks: A Comprehensive Review
Idrees, A. K., Hasan, B. T., & Idrees, S. K. (2012) · Internet of things · doi:10.1007/978-3-031-28631-5_3 · Cited by 1
A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset
Anik, A. S., Chakraborty, K., Datta, B., Kader, M. A., & Morol, M. K. (2022) · International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET) · doi:10.1109/icrpset57982.2022.10188570 · Cited by 1
Enhancing CNN-based Face Mask Detection and Usage Classification using Capsule Networks
Tomas, J. P. Q., Cruz, R. G. B. D., Federez, U. A., & Samonte, D. C. (2023) · International Conference on Digital Image Processing · doi:10.1145/3604078.3604112 · Cited by 1
Automation Model Development for School Reaccreditation of Early Childhood Education
Suhadi, S., Cahyana, A., Agency for National Accreditation, I., Pratama, J. A., Agency for National Accreditation, I., Ramadhan, M. A., Buka Data Indonesia, I., Totalia, S. A., & Wahyudi, S. (2023) · International Journal of Instruction · doi:10.29333/iji.2024.17111a · Cited by 1
Review of Artificial Intelligence-based COVID-19 Detection and A CNN-based Model to Detect Covid-19 from X-Rays and CT images
Ahmed, M., Gilanie, G., Ahsan, M., Ullah, H., & Sheikh, F. A. (2023) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v11i2.1460 · Cited by 1
COVID-CX-Net: A Transfer Learning Approach to Detect COVID-19 Using Chest X-ray Images
Mahdi, A., & Kabir, M. H. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441306 · Cited by 1
Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-Seq feature extraction using K-mers and N-grams
Iqbal, N., & Bhardwaj, A. (2024) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs61402.2024.10481950 · Cited by 1
Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets
Kim, J., Kim, M., Youn, H., Lee, S.-H., Kwon, S., & Park, K. H. (2024) · Applied Sciences · doi:10.3390/app14209396 · Cited by 1
DRA-CN: A Novel Dual-Resolution Attention Capsule Network for Histopathology Image Classification
Tursun, P., Li, S., Li, M., Lv, X., Chen, C., Chen, C., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8496-7_15 · Cited by 1
A MultiResUNet-based Deep Learning Architecture for Accurate Lung Segmentation in Chest X-Rays
Mehta, V., Mathur, A., Tyagi, M., & Rajpoot, A. S. (2025) · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) · doi:10.1109/cictn64563.2025.10932381 · Cited by 1
Enhancement of Chest X‐Ray Images Classification With Fuzzy‐Variable Neural Network Activation Function
Chelghoum, R., Ikhlef, A., & Jacquir, S. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70094 · Cited by 1
From lab to pocket: A novel continual learning-based mobile application for screening COVID-19
Falero, D., Kabir, M. A., & Homaira, N. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112960 · Cited by 3
Deep Learning for COVID-19
Prashanth, B. S., Kumar, M., Thomas, L., Kumar, M., Wu, D., Annappa, B., Hebbar, A., & Murthy, Y. V. S. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-74761-9_23 · Cited by 1
Classification of COVID-19 Cases from X-Ray Images Based on a Modified VGG-16 Model
Kareem, O. S., & Al-Sulaifanie, A. K. (2022) · Traitement du signal · doi:10.18280/ts.390126 · Cited by 1
Deep Learning-Based COVID-19 Detection Using Lung Parenchyma CT Scans
Kaya, Z., Kurt, Z., Işık, Ş., Koca, N., & Çiçek, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0604-6_23 · Cited by 1
A Short Survey on Deep Learning Models for Covid-19 Detection Based on Chest CT and X-ray Images
Wang, W. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-94182-6_39 · Cited by 1
Automation of COVID-19 Disease Diagnosis from Radiograph
Mangond, K., Divya, B. S., Lingham, N. S. R., & Stephan, T. (2023) · Signals and communication technology · doi:10.1007/978-3-031-19752-9_3 · Cited by 1
Chest X-Rays Findings in Patients Positive for COVID 19 at Sheikh Zayed Hospital Lahore
Sheikh, A. Z., Tariq, Z., Noor, S., Ambreen, A., Awan, S., & Amjad, M. (2021) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs211551196
Platforms of IoT for Detection and Diagnosis covid_19 :
Kadim, R. B., & Hussien, N. A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1963/1/012048
Peer Review #3 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.2/reviews/3
Peer Review #3 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.1/reviews/3
Peer Review #1 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.3)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.3/reviews/1
Peer Review #1 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.2/reviews/1
Peer Review #1 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.1/reviews/1
Detection of Covid-19 by Applying a Convolutional Artificial Neural Network in X-ray Images of Lungs
Galindo, G. L., Rivera, E. R., & Anzueto-Ríos, Á. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-89586-0_6
MC-CapsNet : Low-level Pooling and High-level Fusion of Multi-columns Capsule Network
Li, K., Duan, S., Wang, L., Wang, Y., & Wang, S. (2021) · International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc52316.2021.9608464
COVID-19 Identification on Chest X-rays with Deep Learning Technique
Sharma, P., & Bhatt, D. P. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6285-0_10
COVID-19 Detection and Analysis Using Chest X-Ray and CT- Scan Images
Dar, D. A. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.39393
Deep Learning Models Evolution Applied to Biomedical Engineering
Garza-Ulloa, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-820718-5.00012-x
Modify Convolutional Neural Network Model for The Diagnosis of Multi-classes Lung Diseases Covid-19 And Pneumonia Based on X-ray Images
Kareem, O., & Al-Sulaifanie, A. K. (2022) · The Journal of The University of Duhok · doi:10.26682/sjuod.2022.25.1.9
Peer Review #2 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.1/reviews/2
Peer Review #2 of "COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.655v0.2/reviews/2
Covid-19 detection from X-ray images using Customized Convolutional Neural Network
Shafiq, S., Ali, L., Khan, W., Ullah, R., Khan, T., & Alnaiiar, F. (2022) · doi:10.1109/icai55435.2022.9773586
Detection of COVID-19 from X-Ray Images. (c2021)
Kandalaft, J. (2021) · doi:10.26756/th.2022.247
Detection of COVID-19 from X-Ray Images: A Novel Three-Phase Approach Combining an Ensemble of Customized Convolutional Neural Networks with Feature Extraction and FusionPreprint
Kandalaft, J., Germanos, M., & Azar, D. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4088408
MLPA-Net: A Novel Lightweight Multi-Channel Attention Convolutional Network for COVID-19 Image Classification Based on Chest X-RayPreprint
Zhu, L., Wang, M., Chen, W., Zhou, W., & Yu, B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4113639
COVID-19 identification in ct images based on deep learning models: a comparative approach
Shukla, S., Gani, P., Sao, N., & Khushwaha, A. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns7.11433
Biomedical event extraction on input text corpora using combination technique based capsule network
Kumar, R. N. D., Srihari, K., Chakrapani, A., & Viriyasitavat, W. (2022) · Sadhana · doi:10.1007/s12046-022-01978-0
Competitive verse water wave optimisation enabled COVID-Net for COVID-19 detection from chest X-ray images
More, P. S., & Saini, B. S. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2140074
Light Weighted Model Based on Convolutional Neural Network for Detection of COVID-19 Using Chest X-Ray Images
Mehta, J., Pandey, R., & Tewari, R. P. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23724-9_12
Model for movement of individuals in society after the corona pandemic using deep learning algorithms
Abass, N. S., & Sahib, N. M. (2023) · AIP conference proceedings · doi:10.1063/5.0103986
Deep Implicit Supervision with Multiscale Feature Mapping for COVID-19 Detection in Chest Radiography ImagesPreprint
Aloraini, M., Habib, S., Albattah, W., Alsharekh, M. F., Islam, M., & Khan, S. (2023) · Research Square · doi:10.21203/rs.3.rs-2764526/v1
COVID-19 and Viral Pneumonia Classification Using Radiomic Features and Deep Learning
Baffa, M. D. F. O., Martins, F. L. L., Coelho, A. M., & Felipe, J. C. (2022) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis57111.2022.00064
Study and analysis of various COVID-19 prediction techniques using CT images: A challenging overview
Dhamele, S. J., & Niranjana, G. (2023) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc57852.2023.10127326
A Novel Approach For Covid-19 Pandemic Recognition Using Convolutional Neural Network
Sangamesh, H., Viswanatha, V. M., Petli, V., Patil, N., & Jalihal, V. K. (2022) · IEEE North Karnataka Subsection Flagship International Conference (NKCon) · doi:10.1109/nkcon56289.2022.10127038
Deep learning techniques on 3D‐MRI lung images for detection and segmentation of COVID ‐19 virus
Ahmed, J. S. N., Sathik, M. M., Nallaperumal, K., Kannan, K. S., Kumar, P., & Devi, T. A. M. (2023) · Expert Systems · doi:10.1111/exsy.13378

Authors: Toraman, S., Alakus, T. B., & Turkoglu, I.
Affiliations: Fırat University (TR)
ORCID: Toraman, Suat
Alakus, Talha Burak
Venue: Chaos, Solitons & Fractals
Publisher: Elsevier BV
Published: 2020-11
Volume: 140
Pages: 110122
ISSN: 0960-0779 (ISSN-L)
DOI: 10.1016/j.chaos.2020.110122
PMC: PMC7357532
OpenAlex: W3041148517
Cited by: 327
References: 60
Citations by year: 2026: 8 · 2025: 20 · 2024: 29 · 2023: 67 · 2022: 81 · 2021: 106 · 2020: 15
FWCI: 32.58
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Chaos Solitons & Fractals (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Kırklareli University Institutional Repository (Kırklareli University) (submittedVersion, other-oa)
DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: COVID-19 detection, chest X-ray images, capsule networks, computer-aided diagnosis, pneumonia classification, binary classification, multi-class classification, lung infection diagnosis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks