ArticleBiomedical signal processing and control2022
Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 100 citations in OpenAlex.
- Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.Frontiers in systems biology · 2026Review
- Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images.PloS one · 2026Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization.Sensors (Basel, Switzerland) · 2025Article
- Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.PloS one · 2025Article
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- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- Article
- FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.Medical & biological engineering & computing · 2024Article
- Investigation of substituent effects on the electronic structure and antiviral activity of favipiravir derivatives for Covid-19 treatment using DFT and molecular docking.Scientific reports · 2024Article
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
- Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society.PloS one · 2024Article
- Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.Viruses · 2023Article
- Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia.Diagnostics (Basel, Switzerland) · 2023Article
- CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone.Multimedia tools and applications · 2022Article
- CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images.Biomedical signal processing and control · 2022Article
- Sputum smears quality inspection using an ensemble feature extraction approach.Frontiers in public health · 2022Article
- AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.Frontiers in medicine · 2022Article
- Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus disease of humans. The COVID-19 pandemic has spread to every country on the planet and has wreaked havoc on these countries by increasing the number of human deaths, and in addition, caused intense hunger, and lowered economic productivity. Due to a lack of sufficient radiologist, a restricted amount of COVID-19 test kits is available in hospitals, and this is also accompanied by a shortage of equipment due to the daily increase in cases, as a result of increase in the number of persons infected with COVID-19 . Even for experienced radiologists, examining chest X-rays is a difficult task. Many people have died as a result of inaccurate COVID-19 diagnosis and treatment, as well as ineffective detection measures. This paper, therefore presents a unique detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients. To achieve quick diagnosis, a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method is proposed in this paper to help medical experts diagnose COVID-19 disease. The diagnostic performance of the hybrid CNN model and HOG-based method was then evaluated using chest X-ray images collected from University of Gondar and online databases. The experiment was performed using Keras (with TensorFlow as a backend) and Python. After the DCCNet model was evaluated, a 99.9% training accuracy and 98.3% test accuracy was achieved, while a 100% training accuracy and 98.5% test accuracy was achieved using HOG. After the evaluation, the hybrid model achieved 99.97% and 99.67% training and testing accuracy for detection and classification of COVID-19 which was better by 1.37% compared to when features were extracted using CNN and 1.17% when HOG was used. The DCCNet achieved a result that outperformed state-of-the-art models by 6.7%.
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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.