ArticleComputer methods and programs in biomedicine2020
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers, 7 of them syntheses that pooled it.
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Who cites it
196 citing papers in PubMed, 7 syntheses or guidelines pooled it, 628 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.IEEE journal of biomedical and health informatics · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.Journal of healthcare engineering · 2021Pooled it
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps.Medical & biological engineering & computing · 2026Article
- MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.Scientific reports · 2026Article
- CRTSC: Channel-Wise Recalibration and Texture-Structural Consistency Constraint for Anomaly Detection in Medical Chest Images.Sensors (Basel, Switzerland) · 2025Article
- Pulmonary diseases accurate recognition using adaptive multiscale feature fusion in chest radiography.Scientific reports · 2025Article
- Explainable AI for Oral Cancer Diagnosis: Multiclass Classification of Histopathology Images and Grad-CAM Visualization.Biology · 2025Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- A hybrid segmentation and classification CAD framework for automated myocardial infarction prediction from MRI images.Scientific reports · 2025Article
- Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.Nature biomedical engineering · 2025Article
- TransMVAN: Multi-view Aggregation Network with Transformer for Pneumonia Diagnosis.Journal of imaging informatics in medicine · 2025Article
- A comparative analysis of the binary and multiclass classified chest X-ray images of pneumonia and COVID-19 with ML and DL models.Open medicine (Warsaw, Poland) · 2025Article
- Retrospective Development of an AI Model Combining Ultrasound and Clinical Data for Pediatric Appendicitis Differentiation.Emergency medicine international · 2025Article
- Classification of pulmonary diseases from chest radiographs using deep transfer learning.PloS one · 2025Article
136 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveCoronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays.
methodIn particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. RESULTS AND
conclusionExperimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.
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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.