FUTACOVNET: A Deep CNN Network for Detection of Corona Virus (Covid-19) using Chest X-ray Images

Abstract

In December 2019, WHO declared COVID-19 as morbidity and mortality rates continue to soar high with a global cumulative case of 460,280,168 and cumulative mortality of 6,050,018. The standard clinical golden tool mostly used for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR). It is adjudged to be very expensive, less-sensitive, not readily available in hospitals and most significantly, requires the services of a specialized medical expert. X-ray imaging is an easily accessible tool that can be an excellent alternative tool in COVID-19 diagnosis. This paper proposed a technique to automatically predict the presence of COVID-19 pneumonia from digital chest X-ray images using deep learning. Any technological tool that can help in the effective screening of the COVID-19 infection with high level of accuracy is highly required. In this research, the use of transfer learning approach in the rapid and accurate diagnosis of COVID-19 from chest X-ray images is carried out. A new CNN architecture that is trainable optimally while maximizing the detection accuracy is developed. A database was created by combining several public databases and also by collecting images from National Hospital, Abuja. The database contains a mixture of 3616 COVID-19 and 10,192 normal chest X-ray images. The X-ray images were used to train and validate the deep Convolutional
Neural Network (CNN) model. The trained network was then used to classify the normal and COVID-19 patients. The proposed CNN classification accuracy, precision, recall and F1-Score of the model are 96.5%, 96%, 96% and 96% respectively. The model was then compared with the state-of-the-art CNN models and it outperformed all of them. The high accuracy of this model can significantly improve the speed and accuracy of COVID-19 diagnosis in our local hospitals. This would be extremely valuable during an outbreak of pandemicrelated diseases when there are limited facilities and human resources for early diagnosis and management.