A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery

arXiv:2605.20445 · cs.CV, cs.AI · Submitted 2026-05-19 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-05-19

Updated: 2026-08-27

Code: https://github.com/UCSD-AI4H/COVID-CT

Terminology

• Accuracy — 24× in this paper · explained in 1 episode
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Transfer Learning — 5× in this paper · explained in 10 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes

Sources

• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• Xception: Deep Learning with Depthwise Separable Convolutions
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• An Attention Mechanism with Multiple Knowledge Sources for COVID-19 Detection from CT Images
• CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images

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