Improving COVID-19 Detection Through Cooperative Deep-Learning Pipeline for Lung Semantic Segmentation in Medical Imaging

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2024-08-14 DOI:10.1002/ima.23129

Youssef Mourdi, Hanane Allioui, Mohamed Sadgal

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引用次数: 0

Abstract

The global impact of COVID-19 has resulted in millions of individuals being afflicted, with a staggering mortality toll of over 16 000 over a span of 2 years. The dearth of resources and diagnostic techniques has had an impact on both emerging and wealthy nations. In response to this, researchers from the domains of engineering and medicine are using deep learning methods to create automated algorithms for detecting COVID-19. This work included the development and comparison of a collaborative deep-learning model for the identification of COVID-19 using CT scan images, in comparison to previous deep learning-based methods. The model underwent an ablation study using publicly accessible COVID-19 CT imaging datasets, with encouraging outcomes. The suggested model might aid doctors and academics in devising tools to expedite the process of determining the optimal therapeutic approach for health professionals, hence reducing the risk of potential problems.

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通过合作深度学习管道改进 COVID-19 检测，实现医学影像中的肺部语义分割

COVID-19 的全球影响已导致数百万人患病，两年内死亡人数超过 16 000 人，令人震惊。资源和诊断技术的匮乏对新兴国家和富裕国家都造成了影响。为此，来自工程和医学领域的研究人员正在利用深度学习方法创建检测 COVID-19 的自动算法。这项工作包括开发和比较一个协作式深度学习模型，用于利用 CT 扫描图像识别 COVID-19，并与之前基于深度学习的方法进行比较。该模型使用可公开访问的 COVID-19 CT 成像数据集进行了消融研究，结果令人鼓舞。建议的模型可以帮助医生和学者设计工具，加快医疗专业人员确定最佳治疗方法的过程，从而降低潜在问题的风险。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.