COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images
2023 6th International Conference on Information Systems and Computer Networks, ISCON 2023
; 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20242881
ABSTRACT
Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%. . © 2023 IEEE.
classification; COVID-19; CT; Feature extraction; machine learning; Support Vector Machine; Classification (of information); Computerized tomography; Diagnosis; Discrete wavelet transforms; Extraction; Image classification; Learning systems; Support vector machines; Chest CT scans; Co-occurrence; CT-scan images; Feature extraction methods; Features extraction; Gray-level; Machine-learning; matrix; Support vectors machine
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
Idioma:
Inglés
Revista:
2023 6th International Conference on Information Systems and Computer Networks, ISCON 2023
Año:
2023
Tipo del documento:
Artículo
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