Design of a Convolution Neural Network Model to Predict COVID-19

Coronavirus is a type of viral infection. There are many different kinds, and some cause disease. A newly identified coronavirus, has caused a worldwide pandemic of respiratory illness, called COVID-19. When the virus reaches the lungs, and it causes inflammation, resulting in fluid accumulation and difficulty of breathing. When fluid enters the air in the lungs where gas exchange occurs, it leads to low blood oxygen levels. This condition is termed pneumonia. There are about four to five tests which are used to identify the presence of coronavirus in humans but among them, there are only two tests which are recommended by WHO as they take less time and a low risk to identify the virus During recent times there is a fast transmission of Covid-19, And in some countries that are unable to purchase laboratory kits for testing. We aimed to present the use of Machine learning for the high-accuracy detection of Covid-19 using chest X-ray and these images are publicly available. A convolutional neural network with minimized layers is capable of detecting Covid-19 in a limited number of chest X-ray images. This model can also detect SARS, MERS and severe pneumonia using chest X-ray images has life-saving importance for both patients and doctors.

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MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

10.1101/2021.08.04.21261235 ◽

2021 ◽

Author(s):

Liangrui Pan ◽

boya ji ◽

Xiaoqi wang ◽

shaoliang peng

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Test Accuracy ◽

Learning Networks ◽

X Ray ◽

Network Algorithm ◽

Life Saving ◽

Chest X Ray ◽

Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

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Lung Disease Detection Using CNN

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35595 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4042-4049

Author(s):

Veeramalla Sowmya

Keyword(s):

Neural Network ◽

Autonomous System ◽

Respiratory Illness ◽

Bacterial Disease ◽

X Rays ◽

Streptococcus Pneumonia ◽

X Ray ◽

Life Threatening ◽

Chest X Ray ◽

Statistical Results

Covid Pneumonia is a life-threatening bacterial disease in humans that affects one or both lungs and is caused by the bacteria Streptococcus pneumonia. Also known as Covid-19, this is a respiratory illness that was first discovered in Wuhan, China. Expert radiotherapists must evaluate chest X-rays used to diagnose pneumonia. As a result, establishing an autonomous system for detecting pneumonia would be advantageous for treating the condition quickly, especially in distant places. The statistical results show that using pre trained CNN models and supervised classifier algorithms to analyse chest X-ray pictures, specifically to diagnose Pneumonia, can be highly advantageous. By constructing certain convolution neural network designs, we are developing a classifier model that accurately predicts if a person has covid or pneumonia.

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Convolutional Neural Network Based COVID-19 Detection Using Chest X-ray Images

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36995 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 2963-2967

Author(s):

Chetan P. Padole

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Communicable Disease ◽

Respiratory Illness ◽

Special Treatment ◽

Past Medical History ◽

X Ray ◽

Negative Images ◽

Chest X Ray ◽

Elder People

Coronavirus disease 2019 (COVID-19) is a communicable disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan province, China and has resulted in an ongoing pandemic. Most people infected with the Covid-19 virus will experience mild to moderate respiratory illness and recover without requiring any special treatment or medicines. But elder people, who has some past medical history problems like cardiovascular disease, diabetes, cancer etc. are more likely to develop serious illness and want some medical treatment to cure the disease. In this paper, we experimented with applying a Convolutional Neural Network (CNN) algorithm by using a dataset of 760 Chest X-ray images , some of them are covid positive images and remaining are covid negative images. Among 760 images, we have used 80% of Chest X-ray images for training purposes and 20% for testing purposes. After completing the process, we got the accuracy of 92.84%.

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