• Resumo

    Segmentação de Artefatos de Linha B em Imagens de Ultrassonografia Pulmonar Usando DeepLabV3+

    Data de publicação: 03/05/2023

    In recent years, lung ultrasound (LUS) has gained much importance
    in both the clinical and technological environments. This is because
    over the years it has been noticed that the presence of artifacts in
    the images correlates with a series of anomalies or diseases that
    affect the lungs. Therefore, assisting in the segmentation of these
    artifacts is of great clinical interest, as the prior identification of any
    anomaly can prevent its aggravation. Another challenging scenario,
    in particular, is that of emergency units, where fast and accurate
    diagnosis is essential, therefore, developing methods that facilitate
    the task of identifying anomalies through the segmentation of artifacts
    in images is of great importance. In this work, we train and
    compare the performance of three deep neural network architectures
    that can aid in clinical diagnosis by segmenting B-line artifacts
    in LUS images. These trained models are based on semantic segmentation,
    in which they perform semantic labeling at the pixel level.
    The architectures provided by DeepLabV3+ were used, they were:
    Resnet-18, Resnet-50 and Exception. These trained models were
    evaluated using the metric IoU (intersection over union), precision,
    accuracy, and sensitivity. In the end, the model that proved to be
    the most accurate was the one based on the Resnet-18 architecture,
    with an accuracy of 92.32%. Resnet-50 also showed satisfactory
    results, with a precision of 96.07% and a sensitivity of 91.39%.

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