ABSTRACT
A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause;it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions;this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia;they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Viral pneumonia is a disease which occurs in lungs due to bacterial infection. Since middle of December 2019, many cases of pneumonia with unknown cause were found in Wuhan City, China;at present, it has been confirmed that it is a new respiratory disorder caused due to coronavirus infection. Lungs abnormality is highly risky condition in humans;the reduction of the risk is done by enabling quick and efficient treatment. The Covid-19 pneumonia is mimicking viral pneumonia, that is, their symptoms are undistinguished. Lung's abnormality is detected by Computed Tomography (CT) scan images or X-ray images. By viewing the X-rays or CT scan images, even for a well-trained radiologist, it is difficult to detect Covid-19/viral pneumonia. For quick and efficient treatment, it is necessary that proper detection must take place and during this epidemic situation, late detection can lead to doubling of cases;hence, there is a need of proper tool for quick detection of Covid-19/viral pneumonia. This chapter is discussing various AI tools for quick detection as a part of our contribution for quick detection and cure of Covid-19 to front line corona worriers and safety of viral pneumonia patients from Covid-19. The two AI tools are from deep learning (DL), that is, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which are used for the detection of Covid-19/viral pneumonia. The algorithm is trained using available X-ray images of health lungs, viral pneumonia-affected lungs, and Covid-19-affected lungs available through Kaggle and nondisclosed local hospitals or Covid-19 wards. Also transfer learning method is also used for long-lasting validation of the model. The results give us an accuracy for CNN 83.2 to 94.1% results which are also matched with practically tested positive Covid-19 patients using swab tests by doctors. After testing the various models, we also came through that every model of DL has its own specialty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.