PDF(Pubmed)
Abstract：
The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics.
摘要：
世界卫生组织冠状病毒(COVID-19)普遍的健康困境宣布了这场大流行。任何有助于快速检测冠状病毒并具有巨大识别率的科学器具对医生来说都可能过于富有成果。在这种环境下,创新的自动化，如深度学习，机器学习,图像处理和医学图像，如胸部X线摄影（CXR），与COVID-19相反，计算机断层扫描（CT）已成为有希望的解决方案。目前,逆转录-聚合酶链反应(RT-PCR)检测已被用于检测冠状病毒.由于暂停期较高，测试结果和大量假阴性估计，需要替代解决方案。因此，提出了一种基于机器学习的自动算法，用于检测COVID-19和对9个不同数据集的分级。这项研究影响了图像处理和机器学习的授权，以使用CXR和CT医学成像进行快速和明确的冠状病毒检测。这导致早期检测，诊断,并尽早治愈COVID-19。首先,通过归一化对图像进行预处理，以提高图像质量并去除噪声。其次,图像的分割是通过模糊c均值聚类来完成的。然后各种功能，即，统计，纹理，梯度直方图，和离散小波变换被提取(92)并通过主成分分析从特征向量中选择。最后,k-NN,SRC,ANN,支持向量机用于为正常情况做出决策，肺炎,COVID-19阳性患者。该系统的性能已通过k(5)折交叉验证技术进行了验证。所提出的算法达到91.70%(k-最近邻)，94.40%(稀疏表示分类器)，96.16%(人工神经网络)，99.14%(支持向量机)用于COVID检测。结果表明，通过机器学习和图像处理技术，特征组合和选择可以在14.34s内提高性能。在k-NN中，SRC,ANN,和SVM分类器，SVM显示出更有效的结果，这些结果很有希望，并且可以与文献进行比较。与文献综述相比，所提出的方法提高了识别率。因此,提出的算法显示出巨大的潜力，有利于他们的发现放射科医师。此外，在先前的病毒诊断中卓有成效，并将肺炎与COVID-19和其他大流行区分开来。
