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

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 →

• CT
• COVID-19
• Densenet
• EfficientNet
• MobileNet
• NasNet
• Resnet
• Xception
• X-ray

• paper
• product

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