A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
arXiv:2605.20445v1 Announce Type: cross Abstract: COVID-19 was a significant challenge that led to the loss of numerous lives daily. Not only a certain country was involved in this outbreak, but even the world has suffered because of the coronavirus. Imaging techniques using computed tomography (CT) and X-rays of the lungs are the most useful tools for the COVID-19 or any other pandemic disease screening process. Technology today has revolutionized the world by using artificial intelligence to replace manual processes with automated machines, which enable the system to imitate the human brain
The continuous evolution of deep learning techniques allows for increasingly sophisticated applications in medical imaging analysis, making this comparative study timely as AI integration in healthcare accelerates.
This research highlights the growing efficacy of AI in medical diagnostics, demonstrating how advanced models can provide rapid, automated, and potentially more accurate disease screening, particularly for widespread health crises.
The ability to accurately classify diseases like COVID-19 using AI-powered analysis of medical imagery shifts healthcare towards more automated and efficient diagnostic pathways, reducing reliance on manual interpretation.
- · Healthcare AI companies
- · Diagnostic imaging centers
- · Patients
- · Public health organizations
- · Traditional diagnostic methods
- · Manual image analysis specialists
Improved speed and accuracy in diagnosing respiratory illnesses using AI.
Increased demand for robust AI infrastructure and reliable medical imaging datasets in healthcare systems.
The establishment of AI as a standard diagnostic tool across various medical specialties, leading to a paradigm shift in healthcare delivery.
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Read at arXiv cs.AI