The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with respect to prior handcrafted counterparts. In this paper, we propose a novel deep learning framework for Coronavirus detection using CT and X-ray images. In particular, a Vision Transformer architecture is adopted as a backbone in the proposed network, in which a Siamese encoder is utilized. The latter is composed of two branches: one for processing the original image and another for processing an augmented view of the original image. The input images are divided into patches and fed through the encoder. The proposed framework is evaluated on public CT and X-ray datasets. The proposed system confirms its superiority over state-of-the-art methods on CT and X-ray data in terms of accuracy, precision, recall, specificity, and F1 score. Furthermore, the proposed system also exhibits good robustness when a small portion of training data is allocated.

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers / Al Rahhal, M. M.; Bazi, Y.; Jomaa, R. M.; Alshibli, A.; Alajlan, N.; Mekhalfi, M. L.; Melgani, F.. - In: JOURNAL OF PERSONALIZED MEDICINE. - ISSN 2075-4426. - ELETTRONICO. - 12:2(2022), pp. 31001-31017. [10.3390/jpm12020310]

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers

Bazi Y.;Mekhalfi M. L.;Melgani F.
2022-01-01

Abstract

The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with respect to prior handcrafted counterparts. In this paper, we propose a novel deep learning framework for Coronavirus detection using CT and X-ray images. In particular, a Vision Transformer architecture is adopted as a backbone in the proposed network, in which a Siamese encoder is utilized. The latter is composed of two branches: one for processing the original image and another for processing an augmented view of the original image. The input images are divided into patches and fed through the encoder. The proposed framework is evaluated on public CT and X-ray datasets. The proposed system confirms its superiority over state-of-the-art methods on CT and X-ray data in terms of accuracy, precision, recall, specificity, and F1 score. Furthermore, the proposed system also exhibits good robustness when a small portion of training data is allocated.
2022
2
Al Rahhal, M. M.; Bazi, Y.; Jomaa, R. M.; Alshibli, A.; Alajlan, N.; Mekhalfi, M. L.; Melgani, F.
COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers / Al Rahhal, M. M.; Bazi, Y.; Jomaa, R. M.; Alshibli, A.; Alajlan, N.; Mekhalfi, M. L.; Melgani, F.. - In: JOURNAL OF PERSONALIZED MEDICINE. - ISSN 2075-4426. - ELETTRONICO. - 12:2(2022), pp. 31001-31017. [10.3390/jpm12020310]
File in questo prodotto:
File Dimensione Formato  
2022-JPM-Covid-Transformers.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 3.33 MB
Formato Adobe PDF
3.33 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/372922
Citazioni
  • ???jsp.display-item.citation.pmc??? 5
  • Scopus 27
  • ???jsp.display-item.citation.isi??? 19
social impact