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Analysis of institutional authors

Arias-Londono, JdAuthorMoure-Prado, AAuthorGodino-Llorente, JiCorresponding Author

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February 13, 2024
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Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images-Focussing Attention on the Lungs

Publicated to: Diagnostics. 13 (8): 1381- - 2023-04-01 13(8), DOI: 10.3390/diagnostics13081381

Authors:

Arias-Londoño, JD; Moure-Prado, A; Godino-Llorente, JI
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Affiliations

Univ Politecn Madrid, ETSI Telecomunicac, Avda Ciudad Univ 30, Madrid 28040, Spain - Author

Abstract

Due to the primary affection of the respiratory system, COVID-19 leaves traces that are visible in plain chest X-ray images. This is why this imaging technique is typically used in the clinic for an initial evaluation of the patient's degree of affection. However, individually studying every patient's radiograph is time-consuming and requires highly skilled personnel. This is why automatic decision support systems capable of identifying those lesions due to COVID-19 are of practical interest, not only for alleviating the workload in the clinic environment but also for potentially detecting non-evident lung lesions. This article proposes an alternative approach to identify lung lesions associated with COVID-19 from plain chest X-ray images using deep learning techniques. The novelty of the method is based on an alternative pre-processing of the images that focuses attention on a certain region of interest by cropping the original image to the area of the lungs. The process simplifies training by removing irrelevant information, improving model precision, and making the decision more understandable. Using the FISABIO-RSNA COVID-19 Detection open data set, results report that the opacities due to COVID-19 can be detected with a Mean Average Precision with an IoU > 0.5 (mAP@50) of 0.59 following a semi-supervised training procedure and an ensemble of two architectures: RetinaNet and Cascade R-CNN. The results also suggest that cropping to the rectangular area occupied by the lungs improves the detection of existing lesions. A main methodological conclusion is also presented, suggesting the need to resize the available bounding boxes used to delineate the opacities. This process removes inaccuracies during the labelling procedure, leading to more accurate results. This procedure can be easily performed automatically after the cropping stage.
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Keywords

ArticleArtificial intelligenceArtificial neural networkChest x-rayComputer assisted radiographyComputer assisted tomographyConvolutional neural networkCoronavirus disease 2019Covid-19Cross validationDecision support systemDeep learningDigital radiographyFalse positive resultHumanLesions detectionMajor clinical studyMucosaPneumoniaReproducibilityRespiratory systemThorax radiography

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Diagnostics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 59/329, thus managing to position itself as a Q1 (Primer Cuartil), in the category Medicine, General & Internal.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.15. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-27, the following number of citations:

  • WoS: 4
  • Scopus: 5
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 11 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/84850/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 148
    • Downloads: 77
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    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ARIAS LONDOÑO, JULIAN DAVID) and Last Author (GODINO LLORENTE, JUAN IGNACIO).

    the author responsible for correspondence tasks has been GODINO LLORENTE, JUAN IGNACIO.

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    Awards linked to the item

    This research was funded by Comunidad de Madrid (Program: CM-RIS3), grant REACT-CM MadridDataSpace4Pandemics-CM. Funded as a response of the EU to the COVID-19 Pandemics.
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