Diagnosing COVID-19 from X-Ray images with using multi-channel CNN architecture

Covid-19 has been described as a pandemic by the World Health Organization. It has become an epidemic all over the world and has created a risk for people that may lead to death. To diagnose Covid-19, the diagnosis must be confirmed by RT-PCR test. The test takes a long time and false-negative results can be obtained. If the diagnosis of Covid-19 is made early and correct, the ratio of threats to life is reduced. Deep learning has been widely used in a variety of applications to solve a variety of complex problems that require extremely high accuracy and precision, especially in the medical field. In this study, the Covid-19 is diagnosed automatically using a proposed multi-channel CNN method. Patients and healthy individuals' Lung X-ray images datasets were obtained from three separate online databases. Simple recurrent networks (SRN) architecture was also applied for the same problem to compare the results and demonstrate the efficiency of the proposed method. It is to be noted that to reveal the performance, accuracy and efficiency of the study, accuracy and precision analysis and measurements of processing times for the applied methods were performed. With the proposed system Covid-19 is diagnosed in a short time without waiting for the PCR test and precautions are taken before the virus increases its effect on the body and the risk of individuals' life. Differently from the studies in the literature, the multi-channel CNN architecture with five convolution channels is proposed and the channel selection formulas are presented which are used for selecting the most distinctive feature filters among the results produced by these channels.

Automated summary

COVID-19 has been described as a global pandemic which poses a significant threat to life. A multi-channel CNN method has been proposed in this study to automatically diagnose COVID-19, reducing the diagnosis time effectively.