… Ensemble learning helps to reduce this variance by combining predictions of multiple … This
study aims to construct and assess the performance of an ensemble of machine learning (ML) …

… find that ensemble … assessment helps guide algorithm design and parameter optimization.
To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-…

… of abnormalities on chest radiographs. The main … from the ensemble estimates \(\{e^{(k)}\}_{k=1}^{M}\)
via averaging. In our work, we found dropout to be as effective as deep ensembles. …

Chest radiography (CXR) is a popular imaging modality for screening lung abnormalities. It
plays a vital role in monitoring disease progression, allocating limited medical resources …

… Assessment of an ensemble of machine learning models toward abnormality detection in
chest radiographs. In Proceedings of the Annual International Conference of the IEEE …

… an ensemble were selected on the basis of the area under the precision-recall curve in
the validation set. The final models were an ensemble of … of the ensemble (Appendix E1 [online]). …

… chest radiographs for 14 disease classes through a systematic evaluation of a set of candidate
CNN models by adapting them to chest … Assessment of an ensemble of machine learning …

… chest X-ray (CXR), also called chest film or chest radiograph, is the most common imaging
modality used to diagnose conditions affecting the chest … , the authors assessed the accuracy …

… performance level assessment helped guide algorithm design and parameter optimization.
To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-…

… This study used a chest radiograph image dataset collected from a Kaggle repository entitled
“Chest X-rays: bacterial/viral pneumonia/normal,” which was published in October 2020 [6]. …