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. 2024 Mar 11;14(1):5890.
doi: 10.1038/s41598-024-56171-6.

Generalizable disease detection using model ensemble on chest X-ray images

Maider Abad  1 , Jordi Casas-Roma  2   3   4 , Ferran Prados  2   5   6
Affiliations

Generalizable disease detection using model ensemble on chest X-ray images

Maider Abad et al. Sci Rep. .

Abstract

In the realm of healthcare, the demand for swift and precise diagnostic tools has been steadily increasing. This study delves into a comprehensive performance analysis of three pre-trained convolutional neural network (CNN) architectures: ResNet50, DenseNet121, and Inception-ResNet-v2. To ensure the broad applicability of our approach, we curated a large-scale dataset comprising a diverse collection of chest X-ray images, that included both positive and negative cases of COVID-19. The models' performance was evaluated using separate datasets for internal validation (from the same source as the training images) and external validation (from different sources). Our examination uncovered a significant drop in network efficacy, registering a 10.66% reduction for ResNet50, a 36.33% decline for DenseNet121, and a 19.55% decrease for Inception-ResNet-v2 in terms of accuracy. Best results were obtained with DenseNet121 achieving the highest accuracy at 96.71% in internal validation and Inception-ResNet-v2 attaining 76.70% accuracy in external validation. Furthermore, we introduced a model ensemble approach aimed at improving network performance when making inferences on images from diverse sources beyond their training data. The proposed method uses uncertainty-based weighting by calculating the entropy in order to assign appropriate weights to the outputs of each network. Our results showcase the effectiveness of the ensemble method in enhancing accuracy up to 97.38% for internal validation and 81.18% for external validation, while maintaining a balanced ability to detect both positive and negative cases.

Keywords: Domain adaptation; Ensemble classifier; Pre-trained models; Transfer learning; X-ray imaging.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Proposed model ensemble architecture for COVID-19 detection.
Figure 2
Figure 2
Flowchart of proposed transfer learning model.
Figure 3
Figure 3
Confusion matrix of transfer learning models on the internal dataset.
Figure 4
Figure 4
Confusion matrix of transfer learning models on the external dataset.
See this image and copyright information in PMC

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References

    1. Bouchareb Y, et al. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput. Biol. Med. 2021;136:104665. doi: 10.1016/j.compbiomed.2021.104665. - DOI - PMC - PubMed
    1. Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble. Expert Syst. Appl. 2021;165:113909. doi: 10.1016/j.eswa.2020.113909. - DOI - PMC - PubMed
    1. Douglas, L. J. Automated detection of COVID-19 with X-ray images by neural network based algorithms (2021).
    1. Fusco R, et al. Artificial intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine learning and deep learning approaches for diagnosis and treatment. J. Pers. Med. 2021;11:25. doi: 10.3390/jpm11100993. - DOI - PMC - PubMed
    1. DeGrave AJ, Janizek JD, Lee S-I. Ai for radiographic covid-19 detection selects shortcuts over signal. Nat. Mach. Intell. 2021;3:610–619. doi: 10.1038/s42256-021-00338-7. - DOI