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.
Computed Tomography (CT); Real-Time Polymerase Chain Reaction (RT-PCR); X-Radiation (X-Ray); Computerized tomography; Deep learning; Diagnosis; Learning algorithms; Learning systems; Polymerase chain reaction; Transfer learning; Viruses; Computed tomography; Computed tomography scan; Coronaviruses; Learning models; Learning techniques; Real time polymerase chain reactions; Real-time polymerase chain reaction; X-radiation; COVID-19
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
Similar
MEDLINE
...
LILACS
LIS