International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
www.irjet.net
COMPARATIVE ANALYSIS OF FILTERING TECHNIQUES IN CORONA VIRUS PREDICTION SYSTEM Dr. Sunitha M R1, Sana Ara2, Gopalakrishna C3 Arpitha C N4 1professor,
Department of Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikmagalur, Karnataka, India 24th Sem M.Tech Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikmagalur, Karnataka, India 3Associate professor, Department of Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikmagalur, Karnataka, India 4Assistant professor, Department of Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikmagalur, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 symptoms and measures Abstract - Many countries are challenged by the medical sources essential for COVID-19 prediction which requires the evolution of a less-cost, quick tool to determine and identify the virus successfully for a substantial number of tests. `Therefore, a chest CT scan image is a convenient candidate tool that the images developed by the scans should be analyzed precisely and rapidly if bulk numbers of tests are to be treated. Many pre-processing techniques in image processing have been used including image resizing, image enhancement, converting to gray scale, image augmentation etc. To get good accuracy and good results in predicting COVID19, the images used in diagnosing the disease should undergo noise reduction. In this paper we are detailing about pre-processing techniques like mean filter, median filter and Gaussian filter.
The common symptoms of COVID-19 are fever, coughing and sneezing, fever, and difficulty in breathing. Along with these symptoms the other symptoms also include are hearing problems, diarrhea, chest pains, a loss of sense of smell and nasal congestion are experienced. The World health organization has released some preventive measures to avoid infection from COVID-19 virus. The precautionary measures are avoiding handshaking, covering the face with a cloth or mask, enforcing a lockdown and following social distancing.
2. METHODOLOGY Figure-1 depicts block diagram of Corona Virus Prediction System using CNN and SVM.
Key Words: COVID-19, Mean filter, Median filter, Gaussian Filter
Input of CT scan images
1. INTRODUCTION Since December 2019, a COVID-19 termed as severe acute respiratory syndrome coronavirus 2 (SARs-CoV- -2) has led to a fatal disease termed as coronavirus disease (COVID-19). While COVID-19 begin in the city Wuhan, China, the whole world is presently steadily enduring from the disease. SARsCoV-2(severe acute respiratory syndrome) has caused more harmful effects and it killed 773 people, and Middle East respiratory syndrome (MERS-CoV) causing death of 857 people.
Output
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Impact Factor value: 7.529
segment ation
Feature selection
Classification
Figure -1: Methodology of proposed system Preprocessing The input used here is CT scan images. In image processingprocessing is important step because this step helps in extracting right amount of information. In many CT scan images, there is variety of noises which leads to extract wrong information.
As this COVID-19 has many harmful effects and severe transmission capacity, it is necessary to detect the COVID-19 widespread. If the proper projection of the disease is done, then it allows a country to respond pertinently for the near future. However, this prediction of this COVID-19 disease has many many challenges. Many of these challenges it includes are no proper treatment, no proper tracking of infected people and no proper information in the available datasets. Hence, it is important to predict COVID-19.
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Preprocessing
First, the original CT scan RGB image is converted to grayscale image. Once the grayscale image is obtained, filters are applied one after another to reduce noise. Mean and median filters are applied to remove high frequency content
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