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Covid-Classification-and-Severity-Prediction

COVID-19 seems to be an extremely contagious disease and rapid human-to-human transition rate spreading quickly. It is also associated with high ICU admission resulting in an urgent need for development of fast and accurate detection and diagnosis. Identifying positive COVID-19 in early stages helps in isolation and breaking the infection chain.

In this project, we have created the classification model and the severity model for the detection of Covid-19 and evaluation of its severity from chest xrays.

Dataset

Lung Segmentation Dataset

Classification Dataset

Chest Radiography

Severity Dataset

Brixia

Architecture

Classification

Severity

Results

Segmentation Results

Dice Loss: 0.0221 IOU: 0.9790 Recall: 0.9903 Precision: 0.9924

Classification Results

Models Accuracy
Conv-Capsule Network 93.98%
Covid-Net 93.3%
Xception 92.85%
DarkNet 87.02%
CoroNet(Xception) 89.6%

Severity Results

Model MAE (A, B, C, D, E, F) MSE (A, B, C, D, E, F)
ResNet50 0.8509, 0.8513, 0.7953, 0.8434, 0.7910, 0.69 0.9678, 0.9804, 0.8938, 0.9164, 0.8575, 0.695
DenseNet 201 0.4655, 0.7565, 0.7361, 0.8706, 0.4987, 0.6809 0.3351, 0.8612, 0.7463, 0.9684, 0.3874, 0.6442
VGG16 0.9252, 0.9481, 0.9328, 0.9704, 0.8948, 0.8834 1.0761, 1.1396, 1.1211, 1.1391, 1.0231, 1.0268

Models

Find other models at link

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