Diagnosis of Covid-19 Disease by Combining Hand-crafted and Deep-learning Methods on Ultrasound Data

Document Type : Research Paper


1 PhD student of Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz

2 Faculty of Electrical and Computer Engineering, University of Tabriz


Coronavirus 2019, or Covid-19, is an acute respiratory disease with high virus transmission capacity that has led to high mortality rates worldwide. Although rapid diagnosis can play an essential role in the patient's recovery, radiography by the treatment staff is a time-consuming process.Therefore, the use of ultrasound data and deep learning techniques are recommended. The ultrasound technique is radiation-free and can be used in pediatric wards and intensive care units for specific patients. However, its data have noise that affects the performance of deep learning methods. To this end, in this paper, we combine the uniform local noise-resistant binary pattern method with the deep learning method. First, a uniform local binary pattern is calculated on two temporal planes to extract the features of the Covid-19 manifestations in consecutive ultrasound data, and then, the resulting matrix is given as the input of the convolutional network. According to the performed experiments, the proposed method has better performance compared to that of the other state-of-art methods. The results show that the diagnosis accuracy of the Covid-19 from the ultrasound data using the proposed method is 98.5%.