eprintid: 530 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/05/30 datestamp: 2022-03-23 19:46:19 lastmod: 2023-07-04 23:30:09 status_changed: 2022-03-23 19:46:19 type: article metadata_visibility: show creators_name: Kaur, Prabhjot creators_name: Harnal, Shilpi creators_name: Tiwari, Rajeev creators_name: Alharithi, Fahd S. creators_name: Almulihi, Ahmed H. creators_name: Delgado Noya, Irene creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: irene.delgado@uneatlantico.es creators_id: title: A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images ispublished: pub subjects: uneat_bm divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: none keywords: convolutional neural network; COVID-19; disease detection; InceptionV4; SVM; chest XR images 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. date: 2021-11 publication: International Journal of Environmental Research and Public Health volume: 18 number: 22 pagerange: 12191 id_number: doi:10.3390/ijerph182212191 refereed: TRUE issn: 1660-4601 official_url: http://doi.org/10.3390/ijerph182212191 access: open language: en citation: Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés 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. metadata Kaur, Prabhjot; Harnal, Shilpi; Tiwari, Rajeev; Alharithi, Fahd S.; Almulihi, Ahmed H.; Delgado Noya, Irene y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, SIN ESPECIFICAR (2021) A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images. International Journal of Environmental Research and Public Health, 18 (22). p. 12191. ISSN 1660-4601