Late-Ensemble of Convolutional Neural Networks with Test Time Augmentation for Chest XR COVID-19 Detection

  1. Abdul Qayyum
  2. Imran Razzak
  3. Moona Mazher
  4. Domenec Puig

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

COVID-19, a severe acute respiratory syndrome aggressively spread among global populations in just a few months. Since then, it has had four dominant variants (Alpha, Beta, Gamma and Delta) that are far more contagious than original. Accurate and timely diagnosis of COVID-19 is critical for analysis of damage to lungs, treatment, as well as quarantine management [7]. CT, MRI or X-rays image analysis using deep learning provide an efficient and accurate diagnosis of COVID-19 that could help to counter its outbreak. With the aim to provide efficient multi-class COVID-19 detection, recently, COVID-19 Detection challenge using X-ray is organized [12]. In this paper, the late-fusion of features is extracted from pre-trained various convolutional neural networks and fine-tuned these models using the challenge dataset. The DensNet201 with Adam optimizer and EffecientNet-B3 are fine-tuned on the challenge dataset and ensembles the features to get the final prediction. Besides, we also considered the test time augmentation technique after the late-ensembling approach to further improve the performance of our proposed solution. Evaluation on Chest XR COVID-19 showed that our model achieved overall accuracy is 95.67%. We made the code is publicly available 1 . The proposed approach was ranked 6th in Chest XR COVID-19 detection Challenge [1].

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.02.25.22271520: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2022.02.25.22271520v1 on medRxiv