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Deep Learning Models Achieve 98% Accuracy in COVID-19 Image Classification

By PulseAugur Editorial · [1 sources] ·

Researchers have conducted a comprehensive comparison of various deep learning architectures for classifying COVID-19 from CT and X-ray lung imagery. The study utilized pre-trained models including VGG, Densenet, Resnet, MobileNet, Xception, EfficientNet, and NasNet. Results indicated that Resnet and VGG architectures achieved high accuracy, between 95% and 98%, in differentiating COVID-19 positive cases from healthy lungs, outperforming previous literature findings. AI

IMPACT Demonstrates high accuracy of deep learning models in medical image analysis, potentially improving diagnostic speed and accuracy for infectious diseases.

RANK_REASON Academic paper comparing deep learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Learning Models Achieve 98% Accuracy in COVID-19 Image Classification

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Academic paper comparing deep learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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139 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sarmad Khan, Arslan Shaukat, Umer Asgher, Basim Azam ·

    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 comp…

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