X-Ray Classification to Detect COVID-19 using Ensemble Model

Abstract

Diagnosis with medical images has soared to new heights and play massive roles in assisting radiologists to detect and analyse medical conditions. Computer-Aided Diagnosis systems are successfully used to detect tuberculosis, pneumonia, etc. from CXR images. CNNs have been adopted by many studies and achieved laudable results in the field of medical image diagnosis, having attained state-of-art performance by training on labeled data. This paper aims to propose an Ensemble model using a combination of deep CNN architectures, which are Xception, InceptionResnetV2, VGG19, DenseNet-201, and NasNetLarge, using image processing and artificial intelligence algorithms to quickly and accurately identify COVID-19 and other coronary diseases from X-Rays to stop the rapid transmission of the virus. We have used classifiers for the Xception model, VGG19, and InceptionResnet model and compiled a CXR dataset from various open datasets. Since the dataset lacked 1000 viral pneumonia images, we used image augmentation and focal loss to compensate for the unbalanced data and to introduce more variation. After implementing the focal loss function, we got better results. Moreover, we implemented transfer learning using ImageNet weights. Finally, we obtained a training accuracy of 92% to 94% across all models. Our accuracy of the Ensemble Model was 96.25%.