recognition of corona virus using deep learning network

Recognition of Corona virus disease (COVID-19) using deep learning network
Ashwan A. Abdulmunem, Zinah Abulridha Abutiheen, Hiba J. Aleqabie
Department of Computer Science, University of Kerbala, College of Science, Iraq

Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.

A new virus disease spreads last December in Wuhan city in China for uncertain reasons and it was named by the World Health Organization (WHO) as COVID-19 [1]. Because this virus disease is new so there is no medicine is incapable of treating the symptoms whose associated with this virus disease for those people infected by. As a result, thousands of people around the world were passed away and for those who were recovered have different respiratory problems. This virus disease accessed the whole world in a state of panic and fear, and all works outside the home has been stopped as a result of the outbreak of this virus [2].
The incubation period of the virus disease is 1-14 days, at which time its symptoms appear fever, fatigue and dry cough in addition to shortness of breath, in this period the infected people are the source of infection, but it is possible to limit the spread of the virus and prevent infection through the early diagnosis of the disease or the virus carrier and the injured person is isolated and treat the patient [1]. One of the primary treatments is being worked out is recording X-ray for the patients to make sure of the patient’s injury, where a lung part is taken to diagnose the condition of patient by the doctor.
After the revolution in technology is exploded, technology has an effective role in solving and tackle with most of life areas. One of these areas, medical field, where the computer algorithms was used to detect and classify different diseases by using machine learning techniques to analyze X-ray images and give desired results. X-ray considered as a digital image. Digitally, the image is represented by a number of pixels, and each pixel contains a specific value according to the nature of the image (RGB, gray, binary). There are a lot of applications can be implemented to analyze images. One of these applications, segmentation which depends on edges of object and extract common features [2]. There are many researches depend on

segmentation in them researches to reach into specific result such as [3, 4] other researchers worked on review such as [2, 5], also, segmentation challenges have been had in [6]. Main contributions of the work are:
 Working with a vital challenge which is Corona virus (COVID-19) recognition though limited available data, make this problem hard to deal.
 Use recent machine learning algorithms (deep learning) to process and recognize the hottest subject in the world. This work can help the health sector to speedup the diagnosis the disease at its earliest stages.
 Gain significant results to be the work more acceptable and reliable to prove the computerized algorithms can help the community in the epidemic disasters.
The paper organized as following: section 2 reviews the related work and previous researches. Section 3 describes the proposed method: dataset and deep learning networks. While section 4 illustrates the results of the method on COVID-19 dataset. Finally, the conclusion and future work are drawn in section 5.


@ published paper