ArticleJournal of medical and biological engineering2020
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.
Article in Journal of medical and biological engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 3 of them syntheses that pooled it.
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Who cites it
124 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Pooled it
- Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.Journal of healthcare engineering · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Review
- Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.Scientific reports · 2026Article
- CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example.Scientific reports · 2025Article
- Advances in Epstein-Barr Virus Detection: From Traditional Methods to Modern Technologies.Viruses · 2025Review
- Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department.Diseases (Basel, Switzerland) · 2023Article
- Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.Expert systems with applications · 2023Article
- COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.Medical & biological engineering & computing · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- 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
- Utilisation of deep learning for COVID-19 diagnosis.Clinical radiology · 2023Review
- A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms.Multimedia tools and applications · 2023Article
- COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.Cognitive computation · 2022Article
- A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.Arabian journal for science and engineering · 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
64 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWhile the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered.
methodsDeep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks.
resultsTraining the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19.
conclusionThe results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.
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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.