Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

T黵k, Fuat

doi:10.32604/csse.2023.030772

Cited by 1 publication

(1 citation statement)

References 37 publications

(44 reference statements)

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“…In this study, we applied uncertainty-based weighting and entropy calculation to weight the outputs of different networks. Uncertainty-based weighting is a technique that aims to improve the accuracy of ensemble models by assigning different weights to each model output based on its level of uncertainty 40 – 42 . In this case, entropy is used as a measure of uncertainty, with higher entropy indicating greater uncertainty in the model’s predictions.…”

Section: Methodsmentioning

confidence: 99%

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

Abad,

Casas-Roma,

Prados

2024

Sci Rep

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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.

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Section: Methodsmentioning

confidence: 99%