Verma Amar Kumar, Vamsi Inturi, Saurabh Prerna, Sudha Radhika, G R Sabareesh, S Rajkumar
Department of Electrical and Electronics, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, 500078, India.
Department of Mechanical Engineering, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, 500078, India.
Expert Syst Appl. 2021 Dec 15;185:115650. doi: 10.1016/j.eswa.2021.115650. Epub 2021 Aug 2.
This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model's practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.
本文通过对胸部X光图像采用深度学习方法，提出了一种针对严重急性呼吸综合征冠状病毒（SARS-nCoV）患者的基于小波和人工智能的快速高效检测程序。目前，病毒感染主要通过一种基于其基因指纹的实时逆转录聚合酶链反应（rRT-PCR）过程来诊断。整个过程需要大量时间来识别和诊断病毒感染患者。所提出的研究使用基于小波的卷积神经网络架构来检测SARS-nCoV。卷积神经网络在ImageNet上进行预训练，并使用胸部X光图像进行端到端训练。为了执行离散小波变换（DWT），考虑了来自不同族的可用母小波函数，即哈尔小波、达布希耶小波、辛莱特小波、双正交小波、科伊夫曼小波和离散迈耶小波。采用通过离散小波变换进行的两级分解，从胸部X光图像中明确提取下叶常见的外周和胸膜下磨玻璃影的突出特征，以抑制噪声影响，进一步提高信噪比。所提出的基于小波的深度学习模型，无论是两类实例（新冠病毒感染与正常）还是四类实例（新冠病毒感染与细菌性肺炎与病毒性肺炎与正常），都使用k折交叉验证（k-Fold CV）技术从公开可用数据库中进行了验证。除了这些X光图像外，还进一步使用了近期新冠病毒感染患者的图像来检验该模型在应对当前疫情形势下的实用性和实时可行性。观察到具有两级的辛莱特7近似分量表现出最高的测试准确率98.87%，其次是双正交2.6，效率为98.并使用所提出的算法观察到，四类实例的精确率、召回率和骰子相似系数分别为98%、98%和99%。 73%。虽然辛莱特7和双正交2.6的测试准确率很高，但具有两级的哈尔小波和达布希耶小波在未见数据上表现出了出色的验证准确率。
