Domain : Computer Vision, Machine Learning Sub-Domain : Deep Learning, Image Recognition Techniques : Deep Convolutional Neural Network, XceptionNet Application : Image Recognition, Image Classification, Medical Imaging
1. Detected Coronavirus (COVID-19) induced Pneumonia from Chest X-Ray images using Deep Convololutional Neural Network inspired from XceptionNet architecture with 1419 Posterior Anterior (PA) view images of Chest X-ray (COVID-19 : 132 images, Viral Pneumonia : 619 images, Normal/Healthy : 668 images). 2. For classifying Normal, COVID-19 and Viral Pneumonia classes architecture of pretrained network XceptioNet modified. 3. Proposed Modified XceptionNet Network attained testing accuracy of 95.80%, Precision of 96.16%, Sensitivity of 95.60% and F1-score of 95.88%.
Dataset (Normal & Viral cases) : Chest X-Ray Images (Pneumonia) Dataset (Kaggle) Dataset (COVID-19) : COVID Chest xray dataset Our Associated Paper : Diagnosis of Coronavirus Disease (COVID-19) from Chest X-Ray images using modified XceptionNet (Krishna Kant Singh, Manu Siddhartha, Akansha Singh)
The sample images of Normal, Viral and COVID-19 patients are shown in figure below:
Dataset Details
Dataset Name : Chest X-Ray Images (Normal vs COVID-19 vs Viral)
Number of Class : 3
Number/Size of Images : Total : 1419 (555 MB)
Training : 1135
Testing : 284
We have achieved following results which outperform 4 previous state-of-the-art deep CNNs for detection of COVID-10 from CXR.
Performance Metrics 5-Fold Cross Validation Accuracy (100 Epochs) : 96.45% Test Accuracy : 95.88% Precision : 96.16% Sensitivity (COVID-19) : 96% Sensitivity (Viral) : 94% F1-score : 95.88% AUC : 0.99