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

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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]

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

• paper
• product