Haq Amin Ul, Li Jian Ping, Ahmad Sultan, Khan Shakir, Alshara Mohammed Ali, Alotaibi Reemiah Muneer
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia.
Sensors (Basel). 2021 Dec 9;21(24):8219. doi: 10.3390/s21248219.
COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.
新冠肺炎是一种可传播的疾病，也是全球大量人口的主要死因。这种由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的疾病传播速度非常快，会迅速影响人类的呼吸系统。因此，有必要在早期阶段诊断这种疾病，以便进行适当的治疗、康复和控制传播。自动诊断系统对于新冠肺炎检测至关重要。为了从胸部X光图像中诊断新冠肺炎，采用基于人工智能技术的方法更有效，并且能够正确诊断。现有的新冠肺炎诊断方法存在诊断准确性不足的问题。为了解决这个问题，我们提出了一种高效准确的新冠肺炎诊断模型。在所提出的方法中，设计了一个二维卷积神经网络（2DCNN）用于利用胸部X光图像识别新冠肺炎。迁移学习（TL）预训练的ResNet-50模型权重被转移到2DCNN模型中，以增强2DCNN模型的训练过程，并使用胸部X光图像数据进行微调，以进行最终的多分类来诊断新冠肺炎。此外，数据增强技术变换（旋转）用于增加数据集大小，以有效地训练R2DCNNMC模型。实验结果表明，与基线方法相比，所提出的（R2DCNNMC）模型获得了高精度，在CRD数据集上获得了98.12%的分类准确率，在CXI数据集上获得了99.45%的分类准确率。这种方法具有高性能，可用于电子医疗系统中的新冠肺炎诊断。
