COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper, we have proposed a Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images. Multiple state-of-the-art CNN models-DenseNet201, Resnet50V2 and Inceptionv3, have been adopted in the proposed work. They have been trained individually to make independent predictions. Then the models are combined, using a new method of weighted average ensembling technique, to predict a class value. To test the efficacy of the solution we have used publicly available chest X-ray images of COVID +ve and-ve cases. 538 images of COVID +ve patients and 468 images of COVID-ve patients have been divided into training, test and validation sets. The proposed approach gave a classification accuracy of 91.62% which is higher than the state-of-the-art CNN models as well the compared benchmark algorithm. We have developed a GUI-based application for public use. This application can be used on any computer by any medical personnel to detect COVID +ve patients using Chest X-Ray images within a few seconds.

Covid-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper we have proposed a Deep Convolutional Neural Network based solution which can detect the Covid-19 +ve patients using chest X-Ray images. To test the efficacy of the solution we have used publicly available chest X-ray images of Covid +ve and -ve cases. 538 images of Covid +ve patients and 468 images of Covid -ve patients have been divided into 771 trainable images and 235 testing images. Our solution gave a classification accuracy of 95.7% and sensitivity of 98% in the test set-up. We have developed a GUI application for public use. This application can be used on any computer by any medical personnel to detect Covid +ve patients using Chest X-Ray images within a very few seconds.