ArticleOpen medicine (Warsaw, Poland)2025
A comparative analysis of the binary and multiclass classified chest X-ray images of pneumonia and COVID-19 with ML and DL models.
Article in Open medicine (Warsaw, Poland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Emerging Novel SARS-CoV-2 Subvariants and the Advantages of AI-ML in Deciphering the Mutation Trends, Genomic Surveillance and Vaccine Development.Health science reports · 2026Article
- Implementation of a Deep Learning System for Detection and Classification of Lumpy Skin Disease in Cattle: Enhancing Precision and Efficiency in Veterinary Diagnostics.Veterinary medicine and science · 2025Article
- Potential role of different animal models for the evaluation of bioactive compounds.Annals of medicine and surgery (2012) · 2025Review
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
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11 authors.
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Abstract
Background: The highly infectious coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, the seventh coronavirus. It is the longest pandemic in recorded history worldwide. Many countries are still reporting COVID-19 cases even in the fifth year of its emergence. Objective: The performance of various machine learning (ML) and deep learning (DL) models was studied for image-based classification of the lungs infected with COVID-19, pneumonia (viral and bacterial), and normal cases from the chest X-rays (CXRs). Methods: The Results: Among the investigated models, ConvMixer returned the best result in terms of accuracy, recall, precision, Conclusion: The suggested network effectively detected COVID-19 and different types of pneumonia by using CXR imagery. This could help medical sciences for timely and accurate diagnoses of the cases through bioimaging technology and the use of high-end bioinformatics tools.
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