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1.
American Journal of Obstetrics and Gynecology ; 228(1):S76-S76, 2023.
Article in English | Web of Science | ID: covidwho-2310629
2.
2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022 ; : 457-461, 2022.
Article in English | Scopus | ID: covidwho-2213303

ABSTRACT

The novel corona virus (COVID-19), was initially seen in some cities of China in Dec 2019 and then spread exponentially in the entire world and converted into the worldwide pandemic. It rapidly influences and affect day to day life of everybody and slow down economy maximum countries. An immediate requirement raised to detect the positive cases on starting stage and some method to stop further spread. Radiology images have played very important role for detecting COVID-19 and it was found that these images contain very important data which is very much effective in proper diagnosis and treatment. This all creates a requirement of machine learning based artificial intelligent system to detect and further treatment of COVID-19 using X-Ray and CT images and other similar data available. Machine learning based artificial intelligent system can assist and big help for medical staff during diagnoses of COVID-19. This will also be very helpful and fill the gap of shortage of medical staff in interior towns worldwide. As we have seen that COVID-19 virus spread so fast and impact millions of patients in very short time. This creates the requirement of some computerized system that will help in diagnoses and speedy recovery of patients. One another main test which people were using was RT-PCR for detection of COVID-19 but because of many false negative results and time taken in process we need one customized Machine learning based artificial intelligent system that makes use CT images. The proposed system COVID-Rational (COVID-R) is really helpful for early detection of COVID-19 by using classification technique with supervised learning algorithms like random forest and support vector machine (SVM). We have achieved good performance assessment with accuracy of 90.2% for early detection of COVID-19 with our proposed system COVID-R. © 2022 IEEE.

3.
American Journal of Obstetrics and Gynecology ; 224(2):S496-S497, 2021.
Article in English | Web of Science | ID: covidwho-1141124
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