Detection of COVID-19 infection from CT images using the medical photogrammetry technique

Abstract

Medical data such as computed tomography (CT), magnetic resonance imaging (MRI), and Ultrasound images are used in medical photogrammetry. CT images have been used frequently in recent years for the diagnosis of COVID-19 disease, which has contagious and fatal symptoms. CT is an effective method for early detection of lung anomalies due to COVID-19 infection. Machine learning (ML) techniques can be used to detect and diagnose medical diseases. In particular, classification methods are applied for disease diagnosis and diagnosis. This study proposes traditional machine learning algorithms Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes, and an ensemble learning model to detect COVID-19 anomalies using CT images. According to the experimental findings, the proposed ensemble learning model produced an accuracy of 96.71%. This study can help identify the fastest and most accurate algorithm that predicts CT images with Covid-19 during the epidemic process. In addition, machine learning-based approaches can support healthcare professionals and radiologists in the diagnostic phase.

Keywords

• SARS-CoV-2
• Chest CT-scan images
• Medical Photogrammetry
• Machine Learning
• Medical Image Processing

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