TY  - JOUR
ID  - uninimx530
AV  - none
IS  - 22
N2  - 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.
UR  - http://doi.org/10.3390/ijerph182212191
TI  - A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images
KW  - convolutional neural network; COVID-19; disease detection; InceptionV4; SVM; chest XR images
JF  - International Journal of Environmental Research and Public Health
A1  - Kaur, Prabhjot
A1  - Harnal, Shilpi
A1  - Tiwari, Rajeev
A1  - Alharithi, Fahd S.
A1  - Almulihi, Ahmed H.
A1  - Delgado Noya, Irene
A1  - Goyal, Nitin
Y1  - 2021/11//
SN  - 1660-4601
VL  - 18
ER  -