Automated Detection of Pulmonary Pathologies through Deep Learning in Lung Ultrasound
Keywords:
Ultrasound Images, Pulmonary Disease, Deep Learning, Segmentation, Pre-processing, Benchmark Data, AccuracyAbstract
The current surge in newly reported pulmonary diseases and the possibility of further epidemics necessitate the immediate development of a novel Deep Learning (DL) model to facilitate early diagnosis and treatment. Lung ultrasound (LUS) has the potential to detect symptoms of a pulmonary infection, based on growing evidence from various nations. Several characteristics of ultrasonic imaging make it well-suited for routine use: Small hand-held systems, unlike X-ray or computed tomography (CT) equipment, are easier to clean because they are encased in a protective sheath. LUS, on the other hand, enables patient triage in settings other than hospitals, such as tents or homes, and it can detect lung activity during the early stages of the disease while also monitoring affected patients at the bedside on a daily basis. This review paper discusses the potential applications of LUS imaging for disease segmentation and categorization. The paper investigates the open-access LUS dataset and examines image processing algorithms that could increase pulmonary disease detection and segmentation accuracy. We also discuss the many segmentation strategies available for LUS images. Next, we present the currently available DL approaches for LUS image categorization. This survey can be extremely beneficial to researchers struggling with disease diagnosis using LUS images, providing excellent advice on how to proceed with their investigation and determine the source of the problem.
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