ArticleIEEE sensors journal2022
A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling.
Article in IEEE sensors journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition.Computer systems science and engineering · 2023Article
- An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- DeSa COVID-19: Deep salient COVID-19 image-based quality assessment.Journal of King Saud University. Computer and information sciences · 2022Review
- AVNC: Attention-Based VGG-Style Network for COVID-19 Diagnosis by CBAM.IEEE sensors journal · 2022Article
- TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.Computers in biology and medicine · 2022Article
- COVID-19 severity detection using machine learning techniques from CT-images.Evolutionary intelligence · 2022Article
- Machine Vision and Intelligent Algorithm Based on Neural Network.Computational intelligence and neuroscience · 2022Article
- Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier.Signal, image and video processing · 2022Article
- COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing.Arabian journal for science and engineering · 2022Article
- DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification.Frontiers in systems neuroscience · 2022Article
- Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing.Expert systems with applications · 2021Article
- VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.Diagnostics (Basel, Switzerland) · 2021Article
- A Novel Weighted Consensus Machine Learning Model for COVID-19 Infection Classification Using CT Scan Images.Arabian journal for science and engineering · 2021Article
- COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.An international journal on information fusion · 2021Article
- PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.