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S3AR U-Net: A separable squeezed similarity attention-gated residual U-Net for glottis segmentation by Francis Jesmar P. Montalbo is a Medicine article available to read on EtoBox.

What is S3AR U-Net: A separable squeezed similarity attention-gated residual U-Net for glottis segmentation about?

Deep learning in medical imaging became a focal point in research, emphasizing techniques to automatically detect ailments in magnetic resonance images (MRI) and X-rays. Beyond these imaging modalities, there is a recognized need to apply soft computing solutions to Laryngeal Video Endoscopy (LVE), specifically in the context of Videostroboscopy. While this technique offers visual oscillations of the vocal fold, manual examination of the glottis presents inherent challenges. In current literature, various proposals favor segmentation models such as U-Net, trained with labeled data, to address this diagnostic challenge. Fortunately, the Benchmark for Automatic Glottis Segmentation (BAGLS) dataset, comprising 55,750 meticulously labeled images, has become available. However, training a U-Net model with BAGLS requires substantial computational resource investment. Therefore, this study proposes an approach to reduce computational costs by designing a lightweight U-Net incorporating separable depthwise convolutions. In addition to minimizing computational demands, this study enhances the compact architecture by incorporating attention gates, skip connections, and squeeze-andexcitation

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Author

Francis Jesmar P. Montalbo

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Medicine (Physical Sciences)