ArticleInternational journal of imaging systems and technology2022
Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images.
Article in International journal of imaging systems and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Enhancing lung diseases recognition through CNN-RNN methodologies.Scientific reports · 2026Article
- NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.Sensors (Basel, Switzerland) · 2025Article
- A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble.Diagnostics (Basel, Switzerland) · 2024Article
- DLAAD-deep learning algorithms assisted diagnosis of chest disease using radiographic medical images.Frontiers in medicine · 2024Article
- Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis.Sensors (Basel, Switzerland) · 2023Article
- HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network.Sensors (Basel, Switzerland) · 2023Article
- Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.Complex & intelligent systems · 2023Article
- Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.Concurrency and computation : practice & experience · 2022Article
- A Hybrid Deep Learning Model for Brain Tumour Classification.Entropy (Basel, Switzerland) · 2022Article
- A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images.International journal of imaging systems and technology · 2022Article
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4 authors.
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Abstract
By the start of 2020, the novel coronavirus (COVID-19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID-19 rapidly and effectively is by analyzing chest X-ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND-CNN) architecture for the recognition of COVID-19. This network consists of a set of differently-sized hidden layers all created from scratch. The performance of this RND-CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID-19 datasets. Each of these datasets consists of medical images (X-rays) in one of three different classes: chests with COVID-19, with pneumonia, or in a normal state. The proposed RND-CNN model yields encouraging results for its accuracy in detecting COVID-19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID-19 dataset.
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