ArticleApplied soft computing2021
Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 118 citations in OpenAlex.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- CT-based deep learning for preoperative prediction of pathological grading of renal clear cell carcinoma.Frontiers in oncology · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study.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
- Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.Multimedia tools and applications · 2023Article
- Densely attention mechanism based network for COVID-19 detection in chest X-rays.Scientific reports · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture.Intelligent systems with applications · 2022Article
- CodnNet: A lightweight CNN architecture for detection of COVID-19 infection.Applied soft computing · 2022Article
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles.Expert systems with applications · 2022Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.Neural processing letters · 2022Review
- Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.Computers in biology and medicine · 2022Article
- A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images.Biomedical signal processing and control · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.
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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.