Abstract
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.
Highlights
COVID-19 is a transferable illness that is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]
The Coronaviruses are a big family of viruses and SARS-CoV-2 is a ribonucleic acid (RNA) virus that belongs to coronaviruses
Before applying these data sets in the model we need to perform so pre-processing operations on both data sets that model suitable trained for effective performance
Summary
COVID-19 is a transferable illness that is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The efficient and accurate identification of COVID-19 is a big challenge to researchers and medical experts. The Coronaviruses are a big family of viruses and SARS-CoV-2 is a ribonucleic acid (RNA) virus that belongs to coronaviruses. The COVID-19 can be diagnosed through different methods such as medical symptoms (fever, cough, dyspnea, and pneumonia), epidemiological history, positive pathogenic testing, positive chest X-ray, and CT images. The diagnosis of COVID-19, clinical imagining such as chest X-ray, computer tomography (CT), and real-time polymerase chain reaction (RT-PCR) are suitable for accurate and efficient detection. Chest CT scan images are employed to test the severity of lung involvement of COVID-19 positive subjects and provide depth information to analyze the pathogenesis of the disease [2]
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