Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
; 2022.
Artículo
en Inglés
| Scopus | ID: covidwho-20239680
ABSTRACT
The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures:
accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.
Classification; Coronavirus; COVID-19; Ct-Scan images; Linear Regression; Logistic Regression; Computerized tomography; Diagnosis; Image analysis; Image classification; Machine learning; 'Dry' [; Comparative analyzes; Computed tomography scan; Coronaviruses; F1 scores; Imaging tests; Logistics regressions; Reliable detection; Two machines
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio pronóstico
Idioma:
Inglés
Revista:
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
Año:
2022
Tipo del documento:
Artículo
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