X Ray Scans(X 射线扫描)研究综述
X Ray Scans X 射线扫描 - In this research, Convolutional neural network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. [1] In this work, we propose a DL model to automatically segment the knee region and predict onset of Knee OA with X-ray scans. [2] In 2019, coronavirus infection hit Wuhan, China, and spread throughout South Korea, Italy, Iran, the USA, and the rest of the world Medical scientists worked tirelessly to develop vaccines WHO introduced preventive/protective measures to reduce human-human transmission, while researchers are searching for alternative technologies in AI, ML, and feature engineering to improve medical test accuracy, perfect isolation, and disease control Recent research unraveled radiology imaging techniques that predict fast spread and have accurate diagnosis by confirming pathogens in blood cells Machine learning techniques utilized to identify and diagnose COVID-19 are medic-tech applications for analyzing pneumonic effects of the virus in the body ML approaches can help to examine the heart through chest X-rays to reveal how it affects the lungs, kidney, liver, and other vital organs in the body X-ray accuracy can be analyzed with multinomial MNB, ANN, SVM, GANS, and other deep learning tools that validate scan results Data authenticity above 85% may depend on computed algorithms that show whether COVID-19 nucleic acid test results are negative or positive Polymerase chain reaction (PCR) provides a series of DNA samples for performing genetic test analysis PCR helps experts to predict COVID-19 infectious agents using human DNA samples However, PCR that supposes to show how COVID-19 spreads can be influenced by medical protocols causing further contaminations Alternative use of technology has brought about an automated response while using CNN (convolutional neural network) to predict viruses and offer diagnostic options Chest X-ray images can easily be classified using ML fractional multi-channel exponent moments (FrMEMS) to indicate infected patients The relevant approach needed to achieve fractional multi-channels exponent process involves the adoption of parallel computational methods that accelerates, modifies, and optimizes Manta Ray Foraging to improve dataset accuracy from 85% to 98% This report on machine learning techniques for identification and diagnosis of COVID-19, discussed ML performance techniques, identifying image process applications, image classification & analysis with ML, data segmentation, machine learning methods;that classifies x-ray scans, determine rates of COVID-19 spread, normal & abnormal cases, and recommend preventive measures © Springer Nature Switzerland AG 2021. [3] In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. [4] 62% using the chest X-ray scans and requires less computational time. [5] Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. [6] It is confirmed that X-ray scans can be widely used for efficient COVID-19 diagnosis. [7] X-ray scans of the gallium cut hooks depict prominent layering with high Sulfur content for tip cuts and increased calcium and phosphorus content in the base area of the hook. [8] Many researchers have developed frameworks that can automatically detect baggage threats from security X-ray scans. [9] Throughout this work, we have taken the PA read of chest x-ray scans for covid-19 affected patients conjointly as healthy patients. [10] Twelve weeks after implantation, histological analysis and X-ray scans showed that the use of osteoblasts and vascular endothelial cells co-cultured with PCL/nHA/β-TCP scaffolds was sufficient to repair critical defects in rabbit mandibles. [11] These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 patients using X-ray scans. [12] Generally, X-ray scans are cheaper and easily available in most government and private health centres. [13] The secondary outcome measures include knee joint clinical assessments, ratio of relapse, duration of remission, Disease Activity Score in 28 joints (DAS28), inflammation indexes, serum concentrations of specific antibodies, and changes in articular structures as detected by X-ray scans in the 48th week. [14] Pneumonia, a symptom of Covid-19, is a life-threatening condition that affects the lungs and can be detected by analyzing X-ray scans of the chest. [15] Matta’s criteria for pelvic ring fractures may also be useful for predicting the risk of inadequate reduction of the acetabulum on X-ray scans. [16] Providing X-ray scans and describing needle direction and depth of insertion will provide evidence for needling with the arm down as an effective stimulation of the subacromial space. [17] Hence, these studies attract the attention of the computer vision community in integrating X-ray scans and deep-learning-based solutions to aid the diagnosis of COVID-19 infection. [18] We are proposing a deep learning technique to detect the nCovid-19 using frontal Chest X-ray scans. [19] X-ray scans (EDAX) were completed on gallium cut structures (papillae, eggs, male spike and mouth denticles) of B. [20] Thermogravimetric analysis and 3D X-Ray scans of the pore structure were performed on exposed and unexposed samples. [21] First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. [22] As a result, there is increased temporal resolution of the incremental deformation between successive x-ray scanned states allowing synchronized comparison of acoustic emissions to x-ray scans. [23] In end we will create a Convolutional Neural Networks and then we will be able to train it to analyze Chest X-Ray scans with honestly high accuracy. [24] These parametric features, called GMM-CNN, are derived from chest computed tomography (CT) and X-ray scans of patients with coronavirus disease 2019 (COVID-19). [25] By doing so, the model acquires the ability to extract the visual features relevant to the disease during learning and can therefore perform more accurate classification for unseen patients at inference based solely on their X-ray scans. [26] This paper deals with detecting and distinguishing the COVID-19 disease from normal patients through frontal chest X-ray scans using Convolutional Neural Networks. [27] The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. [28] Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-of-the-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. [29] We collect both 2D and 3D X-Ray scans of defective TSVs containing defects such as voids. [30] In this work, we analyze the suitability of three different data valuation methods for medical image classification tasks, specifically pleural effusion, on an extensive data set of chest X-ray scans. [31] This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans. [32] Composition maps of the joint cross sections are compared with the post-process x-ray scans to further examine this technique. [33] Furthermore, the在这项研究中，基于卷积神经网络的模型（一块 VGG、二块 VGG、三块 VGG、四块 VGG、LetNet-5、AlexNet 和 Resnet-50）已被用于检测冠状病毒和 SARS_MERS受感染的患者，使用肺部 X 射线扫描将他们与健康受试者区分开来，由于不同冠状病毒类型的重叠特征，这已被证明是一项具有挑战性的任务。 [1] 在这项工作中，我们提出了一个 DL 模型来自动分割膝关节区域并通过 X 射线扫描预测膝关节 OA 的发作。 [2] 2019 年，冠状病毒感染袭击了中国武汉，并蔓延到韩国、意大利、伊朗、美国和世界其他地区。研究人员正在寻找人工智能、机器学习和特征工程中的替代技术，以提高医学测试的准确性、完美的隔离和疾病控制 最近的研究揭示了放射成像技术，这些技术可以预测快速传播并通过确认血细胞中的病原体来进行准确的诊断 使用机器学习技术识别和诊断 COVID-19 是用于分析病毒在体内的肺炎影响的医疗技术应用 ML 方法可以帮助通过胸部 X 光检查心脏，以揭示它如何影响肺、肾、肝和其他重要器官可以使用多项式 MNB、ANN、SVM、GANS 和其他深度学习工具来分析身体中器官的 X 射线精度扫描结果 85% 以上的数据真实性可能取决于显示 COVID-19 核酸检测结果是阴性还是阳性的计算算法 聚合酶链式反应 (PCR) 提供一系列 DNA 样本进行基因检测分析 PCR 帮助专家预测 COVID -19 使用人类 DNA 样本的感染因子 然而，PCR 应该显示 COVID-19 如何传播可能会受到医疗协议的影响，从而导致进一步的污染并提供诊断选项 胸部 X 射线图像可以使用 ML 分数多通道指数矩 (FrMEMS) 轻松分类，以指示感染患者实现分数多通道指数过程所需的相关方法涉及采用加速的并行计算方法，修改和优化 Manta Ray Foraging 以提高数据集的准确性85% 到 98% 这份关于识别和诊断 COVID-19 的机器学习技术的报告，讨论了 ML 性能技术、识别图像处理应用、使用 ML 进行图像分类和分析、数据分割、机器学习方法；对 X 射线扫描进行分类，确定 COVID-19 传播率、正常和异常病例，并推荐预防措施 © Springer Nature Switzerland AG 2021。 [3] 在本文中，我们使用一种新颖的多尺度轮廓实例分割框架解决了这一挑战，该框架有效地识别了行李 X 射线扫描中杂乱的违禁品数据。 [4] 62% 的人使用胸部 X 光扫描并且需要更少的计算时间。 [5] 最近的某些发现表明，胸部 X 光扫描包含有关病毒发作的重要信息，可以分析这些信息，以便可以在早期阶段开始诊断和治疗。 [6] 已确认 X 射线扫描可广泛用于有效的 COVID-19 诊断。 [7] 镓切割钩的 X 射线扫描描绘了突出的分层，尖端切割的硫含量高，并且钩底部区域的钙和磷含量增加。 [8] 许多研究人员已经开发出可以通过安全 X 射线扫描自动检测行李威胁的框架。 [9] 在整个工作中，我们将受 covid-19 影响的患者的胸部 X 光扫描作为健康患者进行了 PA 读数。 [10] 植入 12 周后，组织学分析和 X 射线扫描表明，使用与 PCL/nHA/β-TCP 支架共培养的成骨细胞和血管内皮细胞足以修复兔下颌骨的严重缺陷。 [11] 这些发现表明深度学习如何显着促进使用 X 射线扫描早期发现 COVID-19 患者。 [12] 一般来说，在大多数政府和私人医疗中心，X 射线扫描更便宜且更容易获得。 [13] 次要结果测量包括膝关节临床评估、复发率、缓解持续时间、28 个关节的疾病活动评分 (DAS28)、炎症指数、血清特异性抗体浓度以及通过 X 射线扫描检测到的关节结构变化。第 48 周。 [14] 肺炎是 Covid-19 的一种症状，是一种危及生命的疾病，会影响肺部，可以通过分析胸部的 X 射线扫描来检测。 [15] Matta 的骨盆环骨折标准也可用于预测 X 射线扫描中髋臼复位不足的风险。 [16] 提供 X 射线扫描并描述针的方向和插入深度将为手臂向下针刺作为对肩峰下空间的有效刺激提供证据。 [17] 因此，这些研究在整合 X 射线扫描和基于深度学习的解决方案以帮助诊断 COVID-19 感染方面引起了计算机视觉界的关注。 [18] 我们正在提出一种深度学习技术，以使用正面胸部 X 射线扫描检测 nCovid-19。 [19] X 射线扫描 (EDAX) 是在 B. [20] 对暴露和未暴露的样品进行热重分析和孔结构的 3D X 射线扫描。 [21] 首先，标记为 COVID-19 感染的可用 X 射线扫描数量相对较少。 [22] 结果，增加了连续 X 射线扫描状态之间的增量变形的时间分辨率，从而允许同步比较声发射与 X 射线扫描。 [23] 最后，我们将创建一个卷积神经网络，然后我们将能够训练它以真正高精度分析胸部 X 射线扫描。 [24] 这些称为 GMM-CNN 的参数特征源自对 2019 年冠状病毒病 (COVID-19) 患者的胸部计算机断层扫描 (CT) 和 X 射线扫描。 [25] 通过这样做，该模型获得了在学习过程中提取与疾病相关的视觉特征的能力，因此可以在仅基于 X 射线扫描的推断中对看不见的患者进行更准确的分类。 [26] 本文涉及使用卷积神经网络通过正面胸部 X 射线扫描检测和区分 COVID-19 疾病与正常患者。 [27] AI 研究界最近一直专注于通过将深度学习技术应用于对 COVID-19 患者进行的 X 射线扫描来诊断 COVID-19。 [28] 在紧迫性的推动下，由于全球患者和死亡人数大幅增加，我们依靠情境实用的胸部 X 光扫描和最先进的深度学习技术为大规模筛查、早期检测、和及时的隔离决策。 [29] 我们收集包含空隙等缺陷的缺陷 TSV 的 2D 和 3D X 射线扫描。 [30] 在这项工作中，我们分析了三种不同数据评估方法对医学图像分类任务（特别是胸腔积液）在广泛的胸部 X 射线扫描数据集上的适用性。 [31] 这一挑战提出了对从 X 射线扫描中对身体部位进行分类的自动化和高效方法的需求。 [32] 将关节横截面的成分图与后处理 X 射线扫描进行比较，以进一步检查该技术。 [33] 此外，<inline-formula> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula>-Bi<sub>2</sub>O<sub>3<基于 /sub> 的 X 射线探测器在 8 个月内无需包装且暴露于相当于 25 000 次胸部 X 射线扫描的 5120 mGy<sub>空气</sub> 剂量下表现出出色的稳定性。 [34] 在 X 射线扫描后获得吸收测量指标，例如腰椎 (LS) 骨矿物质密度 (BMD) 和 LS T 评分。 [35] 在这项工作中，我们对受 covid-19 影响的患者和健康患者进行了胸部 X 射线扫描的 PA 视图。 [36] 采用包括计算机断层扫描 (CT) 和胸部 X 射线扫描在内的放射检查方法来克服 RT-PCR 的缺点。 [37] Matta 的骨盆环骨折标准也可能有助于预测 X 射线扫描中髋臼复位不足的类似风险。 [38] 样品在低温稳定条件下进行调节、运输、储存，然后在 X 射线扫描期间使用特定的低温细胞进行保存。 [39]