Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia.
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia.
PLoS One. 2021 Feb 24;16(2):e0247176. doi: 10.1371/journal.pone.0247176. eCollection 2021.
The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.
2019 年冠状病毒病（COVID-19）的爆发对世界许多国家的健康和日常生活产生了巨大影响。密切观察患者的初始感染部位对于控制 COVID-19 斗争至关重要。早期自动检测新型冠状病毒疾病（COVID-19）将有助于限制其在全球范围内的传播。许多初步研究都集中在鉴定冠状病毒的遗传物质上，对于长期手术的检测率较差。在 COVID-19 治疗中起重要作用的第一种成像方法是胸部 X 射线。放射影像学通常用作强调胸部 X 射线性能的方法。最近的研究结果表明，在胸部 X 射线检查结果不规则的患者中存在 COVID-19。关于这个主题有很多报道，包括使用胸部 X 射线识别 COVID-19 的机器学习策略。其他当前的研究使用了非公开数据集和复杂的人工智能（AI）系统。在我们的研究中，我们提出了一种基于局部加权学习和自组织图（LWL-SOM）策略的新型 COVID-19 识别技术，用于检测和捕获 COVID-19 病例。我们首先使用 SOM 策略根据不同簇中胸部 X 射线数据集的相似特征对图像进行分组，以便区分 COVID-19 和非 COVID-19 病例。然后，我们基于 LWL 算法构建了我们的智能学习模型来诊断和检测 COVID-19 病例。提出的 SOM-LWL 模型提高了 Covid19、无发现和肺炎病例；肺炎和无发现病例；Covid19 和肺炎病例；以及 Covid19 和无发现病例之间的相关系数性能结果；从 0.9613 提高到 0.9788、从 0.6113 提高到 1、从 0.8783 提高到 0.9999、从 0.8894 提高到 1。与使用 AI 评估措施的当前基于机器学习的解决方案相比，提出的 LWL-SOM 在区分 COVID-19 和非 COVID-19 患者方面具有更好的结果。
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