Detection and understanding of objects in an image sequence
No Thumbnail Available
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Université Badji Mokhtar Annaba
Abstract
This study explores the use of object detection and understanding in a series of images to diagnose COVID-19, alongside analyzing individual chest X-rays. By employing convolutional neural networks (CNNs) with adequate preprocessing steps, the model extracts patterns and temporal dependencies from sequential imaging data, aiding in monitoring disease rogression, treatment effectiveness, and predicting outcomes. The COVID-19 pandemic, originating in Wuhan, China, in 2019, has spread to over 200 countries, imposing a significant burden on global health systems and the economy. The pressing need for quicker and more accessible diagnostic methods has become apparent, especially in resource onstrained regions. Early detection can aid in reducing mortality rates and preventing disease transmission. While reverse transcription polymerase chain reaction (RT-PCR) tests have been widely utilized as a reliable method for detecting the virus, they have drawbacks such as being time-consuming, costly, and risky for healthcare workers due to close patient contact. In response to these challenges, medical imaging techniques like X-rays have been employed for COVID-19 screening due to their speed, safety, and wider availability compared to traditional methods. Integrating artificial intelligence (AI) into image processing has shown promising results in accurately distinguishing between normal and diseased chest radiographs, aiding in early diagnosis and patient management. In this study, we propose a deep learning model to analyze COVID-19 chest X-rays, aiming to address the limitations of PCR testing and enhance diagnostic efficiency and challenges including limited training data, impacting model generalization.
Description
Keywords
covid-19; chest xrays; medical imaging; convolutional neural network; lungs segmentation; light enhancement