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A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19
International Journal of Circuit Theory and Applications ( IF 1.8 ) Pub Date : 2022-08-30 , DOI: 10.1002/cta.3417 Rahul Ratnakumar 1 , Satyasai Jagannath Nanda 2
International Journal of Circuit Theory and Applications ( IF 1.8 ) Pub Date : 2022-08-30 , DOI: 10.1002/cta.3417 Rahul Ratnakumar 1 , Satyasai Jagannath Nanda 2
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In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation.
中文翻译：
一种用于快速诊断 COVID-19 的高速无监督硬件架构
在COVID-19的诊断中，血细胞团的调查、分析和自动计数是最重要的步骤。目前采用的细胞分割、识别和计数方法非常耗时，有时需要手动从血涂片样本中进行，这很困难，需要专业实验室技术人员的支持。血细胞计数测试的常规方法是使用自动血液分析仪，该分析仪非常昂贵且速度慢。此外，目前可用的大多数无监督学习技术都假定医生在应用识别之前具有关于图像中可能片段的数量和动作的先验知识。随着疾病的严重程度增加，如 COVID-19、肺癌等的晚期阶段，这种假设最常失败。在这份手稿中，已经确定了一种简化的自动组织病理学图像分析技术及其适用于盲分割、细胞计数和检索半径、面积和周长等细胞参数的硬件架构，不仅可以加快而且可以简化COVID-19 的诊断和预后。这是通过结合三种算法来实现的：K-means 算法，一种新的统计分析技术-HIST（直方图分离技术），以及一种孤岛方法，一种改进版本的 CCA 算法/blob 检测技术。所提出的方法应用于从知名医院数据库中获取的 15 例 COVID-19 慢性呼吸道疾病病例。PSNR、SSIM 等定量参数的输出，定性分析清楚地揭示了该技术在快速细胞学评估中的实用性。所提出的高速和低成本架构在 190 MHz 时钟频率的性能方面给出了有希望的结果，这比其软件实现快两倍。
更新日期：2022-08-30
中文翻译：
一种用于快速诊断 COVID-19 的高速无监督硬件架构
在COVID-19的诊断中，血细胞团的调查、分析和自动计数是最重要的步骤。目前采用的细胞分割、识别和计数方法非常耗时，有时需要手动从血涂片样本中进行，这很困难，需要专业实验室技术人员的支持。血细胞计数测试的常规方法是使用自动血液分析仪，该分析仪非常昂贵且速度慢。此外，目前可用的大多数无监督学习技术都假定医生在应用识别之前具有关于图像中可能片段的数量和动作的先验知识。随着疾病的严重程度增加，如 COVID-19、肺癌等的晚期阶段，这种假设最常失败。在这份手稿中，已经确定了一种简化的自动组织病理学图像分析技术及其适用于盲分割、细胞计数和检索半径、面积和周长等细胞参数的硬件架构，不仅可以加快而且可以简化COVID-19 的诊断和预后。这是通过结合三种算法来实现的：K-means 算法，一种新的统计分析技术-HIST（直方图分离技术），以及一种孤岛方法，一种改进版本的 CCA 算法/blob 检测技术。所提出的方法应用于从知名医院数据库中获取的 15 例 COVID-19 慢性呼吸道疾病病例。PSNR、SSIM 等定量参数的输出，定性分析清楚地揭示了该技术在快速细胞学评估中的实用性。所提出的高速和低成本架构在 190 MHz 时钟频率的性能方面给出了有希望的结果，这比其软件实现快两倍。
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