Automatic image processing to identify post-COVID conditions by using deep learning Article Swipe
Arón Hernández-Trinidad
,
Teodoro Córdova‐Fraga
,
Luis Carlos Padierna
,
José Luis López Hernández
,
Blanca Murillo-Ortíz
,
Rafael Guzmán-Cabrera
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31349/revmexfis.69.061101
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31349/revmexfis.69.061101
In the present research, a supervised learning classification methodology is proposed to identify post-COVID conditions. Image processing and deep learning methods were employed to analyze a data set provided by the High Specialty Medical Unit No.1 of the Mexican Institute of Social Security (T1-IMSS) of Leon, Guanajuato, Mexico, of Mexican patients infected with COVID-19. The dataset is classified into post-COVID findings and no post-COVID findings. A deep neural network of 50 hidden layers is used to extract regions of interest, with properties that can potentially be related to computer-aided medical diagnosis. Different patterns were found in the post-COVID computed tomography scans: pulmonary fibrosis, ground glass pattern, etc. The efficiency of the proposed method was 97% precision using the cross-validation classification scenario. This result allows to provide an auxiliary tool in medical diagnosis, through computer-aided diagnosis. This model provides an automatic and objective estimation of post-COVID conditions of Mexican patients, facilitating the expert interpretation during the COVID-19 pandemic.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Artificial intelligence
Deep learning
Computer science
Artificial neural network
Field (mathematics)
Data set
Machine learning
Image processing
Set (abstract data type)
Medical diagnosis
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Pattern recognition (psychology)
Image (mathematics)
Data mining
Medicine
Pathology
Mathematics
Infectious disease (medical specialty)
Programming language
Pure mathematics
Disease
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.31349/revmexfis.69.061101
- https://rmf.smf.mx/ojs/index.php/rmf/article/download/6719/6829
- OA Status
- diamond
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388231515
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388231515Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31349/revmexfis.69.061101Digital Object Identifier
- Title
-
Automatic image processing to identify post-COVID conditions by using deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-01Full publication date if available
- Authors
-
Arón Hernández-Trinidad, Teodoro Córdova‐Fraga, Luis Carlos Padierna, José Luis López Hernández, Blanca Murillo-Ortíz, Rafael Guzmán-CabreraList of authors in order
- Landing page
-
https://doi.org/10.31349/revmexfis.69.061101Publisher landing page
- PDF URL
-
https://rmf.smf.mx/ojs/index.php/rmf/article/download/6719/6829Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://rmf.smf.mx/ojs/index.php/rmf/article/download/6719/6829Direct OA link when available
- Concepts
-
Coronavirus disease 2019 (COVID-19), Artificial intelligence, Deep learning, Computer science, Artificial neural network, Field (mathematics), Data set, Machine learning, Image processing, Set (abstract data type), Medical diagnosis, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Pattern recognition (psychology), Image (mathematics), Data mining, Medicine, Pathology, Mathematics, Infectious disease (medical specialty), Programming language, Pure mathematics, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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