1,136 research outputs found
Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images
Since 2019, the global dissemination of the Coronavirus and its novel strains
has resulted in a surge of new infections. The use of X-ray and computed
tomography (CT) imaging techniques is critical in diagnosing and managing
COVID-19. Incorporating artificial intelligence (AI) into the field of medical
imaging is a powerful combination that can provide valuable support to
healthcare professionals.This paper focuses on the methodological approach of
using machine learning (ML) to enhance medical imaging for COVID-19
diagnosis.For example, deep learning can accurately distinguish lesions from
other parts of the lung without human intervention in a matter of
minutes.Moreover, ML can enhance performance efficiency by assisting
radiologists in making more precise clinical decisions, such as detecting and
distinguishing Covid-19 from different respiratory infections and segmenting
infections in CT and X-ray images, even when the lesions have varying sizes and
shapes.This article critically assesses machine learning methodologies utilized
for the segmentation, classification, and detection of Covid-19 within CT and
X-ray images, which are commonly employed tools in clinical and hospital
settings to represent the lung in various aspects and extensive detail.There is
a widespread expectation that this technology will continue to hold a central
position within the healthcare sector, driving further progress in the
management of the pandemic
Current Diagnostic Techniques for Pneumonia:A Scoping Review
Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images
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