Computer Sciences Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Morocco.
Polydisciplinary Faculty Safi, Cadi Ayyad University, Morocco.
Radiography (Lond). 2023 Jan;29(1):109-118. doi: 10.1016/j.radi.2022.10.010. Epub 2022 Oct 24.
With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However, strong detection tools can help avoid damage to chest tissues. The proposed method provides an important way to enhance the semantic segmentation process using combined potential deep learning (DL) modules to increase consistency. Based on Covid-19 CT images, this work hypothesized that a novel model for semantic segmentation might be able to extract definite graphical features of Covid-19 and afford an accurate clinical diagnosis while optimizing the classical test and saving time.
CT images were collected considering different cases (normal chest CT, pneumonia, typical viral causes, and Covid-19 cases). The study presents an advanced DL method to deal with chest semantic segmentation issues. The approach employs a modified version of the U-net to enable and support Covid-19 detection from the studied images.
The validation tests demonstrated competitive results with important performance rates: Precision (90.96% ± 2.5) with an F-score of (91.08% ± 3.2), an accuracy of (93.37% ± 1.2), a sensitivity of (96.88% ± 2.8) and a specificity of (96.91% ± 2.3). In addition, the visual segmentation results are very close to the Ground truth.
The findings of this study reveal the proof-of-principle for using cooperative components to strengthen the semantic segmentation modules for effective and truthful Covid-19 diagnosis.
This paper has highlighted that DL based approach, with several modules, may be contributing to provide strong support for radiographers and physicians, and that further use of DL is required to design and implement performant automated vision systems to detect chest diseases.
随着新冠病例数量的增加和治疗费用的增加，胸部疾病在多个社区引起了越来越多的关注，特别是在医学和计算机视觉领域。临床和分析检查被广泛认为是诊断和处理新冠病例的技术。然而，强大的检测工具可以帮助避免胸部组织的损伤。本研究提出的方法提供了一种重要的方法，通过结合潜在的深度学习（DL）模块来增强语义分割过程，以提高一致性。本研究基于新冠 CT 图像，假设一种新的语义分割模型能够提取新冠的明确图形特征，并在优化经典测试和节省时间的同时提供准确的临床诊断。
收集了不同病例（正常胸部 CT、肺炎、典型病毒病因和新冠病例）的 CT 图像。本研究提出了一种先进的 DL 方法来解决胸部语义分割问题。该方法采用改进的 U-net 版本，从研究图像中实现新冠的检测。
验证测试结果具有竞争力，重要的性能指标为：精度（90.96%±2.5），F 分数（91.08%±3.2），准确率（93.37%±1.2），敏感性（96.88%±2.8）和特异性（96.91%±2.3）。此外，分割结果与 Ground truth 非常接近。
本研究的结果证明了使用协作组件来增强语义分割模块以进行有效和真实的新冠诊断的原理。
本文强调了基于深度学习的方法，通过多个模块，可能为放射科医生和医生提供有力支持，并且需要进一步使用深度学习来设计和实施高性能的自动视觉系统来检测胸部疾病。
