COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning

Saha, Prottoy; Aranya, O.F.M. Riaz Rahman; Jahan, Samsad; Islam, Ferdib-Al

doi:10.1016/j.imu.2021.100741

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Convolutional Neural Networks (CNN) have especially achieved great success in COVID-19 diagnosis using radiological imaging, such as CXR radiographs and CT [Arias-Garzón et al 2021], [Saha et al 2021] and [Khasawneh et al 2021]. Despite this success, the literature indicates that several works may suffer from biases or issues in terms of generalization ability (generalizability) [Roberts et al 2021].…”

Section: Introductionmentioning

confidence: 99%

Generalizability of CNN on Predicting COVID-19 from Chest X-ray Images

Freire¹,

Leão²,

Tiago³

et al. 2022

Anais Do XXII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2022)

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Machine learning methods have been applied to predict COVID-19 using chest X-ray images in several works. However, to be helpful, a machine learning model must be robust to give reliable predictions for any target population, rather than only for the population used to generate the training data. Despite such an important issue, testing the generalizability of machine learning models is frequently not performed in current works. To test the generalizability of three models of CNN, four different databases obtained from various data sources are investigated in this paper in an internal-and-external validation procedure. All models are trained considering lung segmentation as a pre-processing step and without lung segmentation. The results show how important an external evaluation is to avoid providing performance evaluations excessively optimistic and inaccurate.

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