Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network

Hu, Xinxin; Zhou, Ru; Maoneng, Hu; Wen, Jing; Shen, Tong

doi:10.1016/j.cmpb.2022.107098

Cited by 6 publications

(1 citation statement)

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“…Section IV presents a comprehensive evaluation of the model, employing various metrics to assess performance against traditional U-Net and other prevalent models. Finally, Section V discusses the implications of our findings for clinical applications and future research directions, followed by a discussion and conclusion in Section VI that encapsulates the study's contributions to the field of medical image segmentation [12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

Saimassay,

Begenov,

Sadyk

et al. 2024

ijacsa

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In the expanding field of medical imaging, precise segmentation of anatomical structures is critical for accurate diagnosis and therapeutic interventions. This research paper introduces an innovative approach, building upon the established U-Net architecture, to enhance lung segmentation techniques applied to Computed Tomography (CT) images. Traditional methods of lung segmentation in CT scans often confront challenges such as heterogeneous tissue densities, variability in human anatomy, and pathological alterations, necessitating an approach that embodies greater robustness and precision. Our study presents a modified U-Net model, characterized by an integration of advanced convolutional layers and innovative skip connections, improving the reception field and facilitating the retention of high-frequency details essential for capturing the lung's intricate structures. The enhanced U-Net architecture demonstrates substantial improvements in dealing with the subtleties of lung parenchyma, effectively distinguishing between precarious nuances of tissues, and pathologies. Rigorous quantitative evaluations showcase a significant increase in the Dice coefficient and a decrease in the Hausdorff distance, indicating a more refined segmentation output compared to predecessor models. Additionally, the proposed model manifests exceptional versatility and computational efficiency, making it conducive for real-time clinical applications. This research underlines the transformative potential of employing advanced deep learning architectures for biomedical imaging, paving the way for early intervention, accurate diagnosis, and personalized treatment paradigms in pulmonary disorders. The findings have profound implications, propelling forward the nexus of artificial intelligence and healthcare towards unprecedented horizons.

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Section: Introductionmentioning

confidence: 99%