Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications
Computer Engineering and Applications Journal
; 12(2):71-78, 2023.
Artículo
en Inglés
| ProQuest Central | ID: covidwho-20242189
ABSTRACT
COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.
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Colección:
Bases de datos de organismos internacionales
Base de datos:
ProQuest Central
Tipo de estudio:
Estudios diagnósticos
/
Estudio experimental
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Computer Engineering and Applications Journal
Año:
2023
Tipo del documento:
Artículo
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