@article{uninimx530,
           month = {Noviembre},
          number = {22},
           title = {A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images},
           pages = {12191},
         journal = {International Journal of Environmental Research and Public Health},
          volume = {18},
          author = {Prabhjot Kaur and Shilpi Harnal and Rajeev Tiwari and Fahd S. Alharithi and Ahmed H. Almulihi and Irene Delgado Noya and Nitin Goyal},
            year = {2021},
        abstract = {COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country?s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named ?C19D-Net?, to detect ?COVID-19? infection from ?Chest X-Ray? (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (?C19D-Net?) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24\% for 4-classes, 95.51\% for three-classes, and 98.1\% for two-classes. The proposed method has outperformed well in expressions of ?precision?, ?accuracy?, ?F1-score? and ?recall? in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed ?C19D-Net? can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.},
        keywords = {convolutional neural network; COVID-19; disease detection; InceptionV4; SVM; chest XR images},
             url = {http://repositorio.unini.edu.mx/id/eprint/530/}
}