Elpeltagy Marwa, Sallam Hany
Systems and Computers Department, Al-Azhar University, Nasr City, Cairo Egypt.
Egyptian Nuclear and Radiological Regulatory Authority, Nasr City, Cairo Egypt.
Multimed Tools Appl. 2021;80(17):26451-26463. doi: 10.1007/s11042-021-10783-6. Epub 2021 May 4.
Recently coronavirus 2019 (COVID-2019), discovered in Wuhan city of China in December 2019 announced as world pandemic by the World Health Organization (WHO). It has catastrophic impacts on daily lives, public health, and the global economy. The detection of coronavirus (COVID- 19) is now a critical task for medical specialists. Laboratory methods for detecting the virus such as Polymerase Chain Reaction, antigens, and antibodies have pros and cons represented in time required to obtain results, accuracy, cost and suitability of the test to phase of infection. The need for accurate, fast, and cheap auxiliary diagnostic tools has become a necessity as there are no accurate automated toolkits available. Other medical investigations such as chest X-ray and Computerized Tomography scans are imaging techniques that play an important role in the diagnosis of COVID- 19 virus. Application of advanced artificial intelligence techniques for processing radiological imaging can be helpful for the accurate detection of this virus. However, Due to the small dataset available for COVID- 19, transfer learning from pre-trained convolution neural networks, CNNs can be used as a promising solution for diagnosis of coronavirus. Transfer learning becomes an effective mechanism by transferring knowledge from generic object recognition tasks to domain-specific tasks. Hence, the main contribution of this paper is to exploit the pre-trained deep learning CNN architectures as a cornerstone to enhance and build up an automated tool for detection and diagnosis of COVID- 19 in chest X-Ray and Computerized Tomography images. The main idea is to make use of their convolutional neural network structure and its learned weights on large datasets such as ImageNet. Moreover, a modification to ResNet50 is proposed to classify the patients as COVID infected or not. This modification includes adding three new layers, named, Conv, Batch_Normaliz and Activation_Relu layers. These layers are injected in the ResNet50 architecture for accurate discrimination and robust feature extraction. Extensive experiments are carried out to assess the performance of the proposed model on COVID- 19 chest X-Ray and Computerized Tomography scan images. Experimental results approve that the proposed modification, injected layers, increases the diagnosis accuracy to 97.7 for Computerized Tomography dataset and 97.1 for X-Ray dataset which is superior compared to other approaches.
2019年12月在中国武汉市发现的新型冠状病毒2019（COVID - 2019）被世界卫生组织（WHO）宣布为全球大流行疾病。它对日常生活、公共卫生和全球经济都产生了灾难性影响。冠状病毒（COVID - 19）的检测如今是医学专家的一项关键任务。用于检测该病毒的实验室方法，如聚合酶链反应、抗原和抗体检测，在获取结果所需时间、准确性、成本以及检测对感染阶段的适用性等方面都各有利弊。由于目前没有准确的自动化检测工具包，因此对准确、快速且廉价的辅助诊断工具的需求变得十分必要。其他医学检查，如胸部X光和计算机断层扫描，是在COVID - 19病毒诊断中发挥重要作用的成像技术。应用先进的人工智能技术处理放射影像有助于准确检测这种病毒。然而，由于可用于COVID - 19的数据集较小，从预训练的卷积神经网络（CNN）进行迁移学习可作为诊断冠状病毒的一种有前景的解决方案。迁移学习通过将知识从通用目标识别任务转移到特定领域任务而成为一种有效机制。因此，本文的主要贡献在于利用预训练的深度学习CNN架构作为基石，增强并构建一个用于在胸部X光和计算机断层扫描图像中检测和诊断COVID - 19的自动化工具。主要思路是利用其卷积神经网络结构及其在诸如ImageNet等大型数据集上学习到的权重。此外，还对ResNet50提出了一种修改方案，用于将患者分类为是否感染COVID。此修改包括添加三个新层，即卷积层（Conv）、批归一化层（Batch_Normaliz）和激活整流线性单元层（Activation_Relu）。这些层被注入到ResNet50架构中以进行准确判别和强大的特征提取。开展了广泛实验来评估所提出模型在COVID - 胸部X光和计算机断层扫描图像上的性能。实验结果表明，所提出的修改，即注入的层，将计算机断层扫描数据集的诊断准确率提高到了97.7%，将X光数据集的诊断准确率提高到了97.1%，与其他方法相比更具优势。
