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POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models
2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022 ; : 12-13, 2022.
Article in English | Scopus | ID: covidwho-2018983
ABSTRACT
The novel coronavirus disease (COVID-19) constitutes a public health emergency globally. It is a deadly disease which has infected more than 230 million people worldwide. Therefore, early and unswerving detection of COVID-19 is necessary. Evidence of this virus is most commonly being tested by RT-PCR test. This test is not 100% reliable as it is known to give false positives and false negatives. Other methods like X-Ray images or CT scans show the detailed imaging of lungs and have been proven more reliable. This paper compares different deep learning models used to detect COVID-19 through transfer learning technique on CT scan dataset. VGG-16 outperforms all the other models achieving an accuracy of 85.33 % on the dataset. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022 Year: 2022 Document Type: Article