ArticleJournal of King Saud University. Computer and information sciences2022
Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features.
Article in Journal of King Saud University. Computer and information sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Ensemble model for grape leaf disease detection using CNN feature extractors and random forest classifier.Heliyon · 2024Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 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
- Optimal Ensemble learning model for COVID-19 detection using chest X-ray images.Biomedical signal processing and control · 2023Article
- COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control.Frontiers in artificial intelligence · 2023Review
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles.Expert systems with applications · 2022Article
- Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images.Journal of computational science · 2022Article
- Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images.Journal of King Saud University. Computer and information sciences · 2022Article
- Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification.Concurrency and computation : practice & experience · 2022Article
- Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.IEEE journal of translational engineering in health and medicine · 2022Article
- Co-Yudh: A Convolutional Neural Network (CNN)-Inspired Platform for COVID Handling and Awareness.SN computer science · 2022Article
- A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.Journal of grid computing · 2022Article
- Artificial intelligence-driven assessment of radiological images for COVID-19.Computers in biology and medicine · 2021Review
- Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images.Biomedical signal processing and control · 2021Article
- Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.
Indexed as
Identifiers
What OpenQuestion holds
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.