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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 ; : 408-412, 2022.
Article in English | Scopus | ID: covidwho-2152477


The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%. © 2022 IEEE.

Periodicals of Engineering and Natural Sciences ; 9(3):29-40, 2021.
Article in English | Scopus | ID: covidwho-1329262


Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19. © 2021. All Rights Reserved.