ArticleCognitive computation2021
Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays.
Article in Cognitive computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 114 citations in OpenAlex.
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.BMC medical imaging · 2024Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- A COVID-19 medical image classification algorithm based on Transformer.Scientific reports · 2023Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 2023Article
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.International journal of data science and analytics · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.Wireless personal communications · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays.Expert systems with applications · 2022Article
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- A Multi-Purpose Shallow Convolutional Neural Network for Chart Images.Sensors (Basel, Switzerland) · 2022Article
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.Neural processing letters · 2022Review
- Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.Information sciences · 2022Article
- A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.Neurocomputing · 2022Article
- SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images.Knowledge-based systems · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works.
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Registered trials
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.