Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset
2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023
; : 225-230, 2023.
Article
in English
| Scopus | ID: covidwho-20231843
ABSTRACT
As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset. © 2023 IEEE.
and artificial intelligence; CNN; dataset; Deep Learning (DL); ECNN; health data analytics; Machine Learning (ML); Computerized tomography; Convolutional neural networks; Data Analytics; Deep learning; Image enhancement; Multilayer neural networks; Polymerase chain reaction; Viruses; Convolutional neural network; Health data; Health data analytic; Machine learning; Machine-learning; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Prognostic study
Language:
English
Journal:
2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023
Year:
2023
Document Type:
Article
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