Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision
2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023
; : 591-595, 2023.
Article
in English
| Scopus | ID: covidwho-2326044
ABSTRACT
The Corona Virus (COVID 19) pandemic is quickly becoming the world's most deadly disease. The spreading rate is higher and the early detection helps in faster recovery. The existence of COVID 19 in individuals shall be detected using molecular analysis or through radiographs of lungs. As time and test kit are limited RT- PCR is not suitable to test all. The RT- PCR being a time-consuming process, diagnosis using chest radiographs needs no transportation as the modern X-ray systems are digitized. Deep learning takes an edge over other techniques as it deduces the features automatically and performs massively parallel computations. Multiple feature maps will help in accurate prediction. The objective of the proposed work is to develop a Computer Aided Deep Learning System identify and localize COVID-19 virus from other viruses and pneumonia. It helps to detect COVID-19 within a short period of time thereby improving the lifetime of the individuals. SIIM-FISABIO-RSNA benchmark datasets are used to examine the proposed system. Recall, Precision, Accuracy-rate, and F-Measure are the metrics used to prove the integrity of the system. © 2023 IEEE.
COVID-19 Detection; Localization of COVID-19 using Lung CT images; Medical Image Processing; Reverse Transcription-Polymerase Chain Reaction [RT-PCR]; you only look once [yolo]; Computer vision; Computerized tomography; Deep learning; Diagnosis; Learning systems; Medical imaging; Polymerase chain reaction; Radiography; Chest radiographs
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023
Year:
2023
Document Type:
Article
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