Diagnosis of Covid-19 using optimized convolutional neural network

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

mohammad fatehi ¹ , mehdi taghizadeh ² , mohammad moradi ³ , gholamhosein shojaat ⁴

1 - 4. Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
2 - Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
3 - Department of Electrical Engineering, chamran Branch, chamran University, Kerman, Iran
4 - Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

Received: 2023-06-13 Accepted : 2023-08-20 Published : 2023-03-01

Keywords: X-ray images, deep learning, Covid 19 disease, optimized convolutional neural network,

Abstract :

According to the report of the World Health Organization, corona disease is the most dangerous and contagious disease in the world. Currently, the most common method used to diagnose corona disease is the polymer chain reaction laboratory technique of reverse transcription, but since this method requires time to confirm the presence of the virus in the laboratory and also due to the unavailability of diagnostic kits and its high costs, Suspected corona virus patients cannot be identified and treated in time; This, in turn, can increase the likelihood of spreading the disease.Another diagnostic method is the use of X-ray chest imaging technique as well as chest computed tomography scan. Also, the use of deep learning methods can be very important for faster and more accurate diagnosis of the lung problems of the corona virus.In this study, using optimized deep convolutional networks based on X-ray images, patients with corona virus were diagnosed.In this article, using the optimized convolutional neural network of healthy people and those with corona, with 10-Fold cross-validation, average accuracy of 98.9% and average sensitivity of 96.5% were obtained.According to the obtained results, it can be said that the proposed method has the ability to separate healthy and unhealthy signals with acceptable accuracy.