A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality

Bhardwaj, Prashant; Kaur, Amanpreet

With the exponential growth of COVID‐19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID‐19, 2022 pneumonia, and 5863 normal chest X‐ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre‐processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.