A MODIFIED DEEP CONVOLUTIONAL NETWORK FOR DETECTION OF COVID19 FROM CHEST X-RAYS BASED ON CONCATENATION OF IMAGE PREPROCESSING TECHNIQUES AND RESnCOV

Abstract

The fast-spreading coronavirus disease called COVID-19 has impacted millions of people worldwide. It becomes difficult for medical experts to rapidly detect the illness and stop its spread because of its rapid growth and rising numbers. One of the newer areas of study where this issue can be more carefully addressed is medical image analysis. In this study, we implemented an image processing system utilizing deep learning and neural networks to previse the 2019-nCoV using chest roentgen ray images. In order to recognize COVID-19 positive and healthy patients using chest roentgen ray images, this paper suggests employing convolutional neural networks, deep learning, and machine learning. We proposed a neural network composed of various features taken from two convolutional neural networks, ResNet50 and ResNet152V2, in order to successfully manage the intricate structural complexity of an image. We tested our network on 7940 images to see how well it performs in real-world situations. The proposed network detects normal and COVID-19 cases with an average accuracy of 95% and can be used as an aid in the radiology department