Аннотации:
Global healthcare systems are facing unprecedented challenges as a result of the
COVID-19 pandemic, which calls for quick and precise diagnostic instruments to stop its
spread. In this study, we investigate the application of transfer learning in convolutional
neural networks (CNNs) and the influence of data augmentation in de- tecting COVID-19
from chest X-ray images. We leverage a dataset comprising images from patients
diagnosed with COVID-19 and images corresponding to non-COVID cases, and as part
of a transfer learning approach, we utilize four well known CNN models: ResNet50,
MobileNet V2, EfficientNet V2, and MobileNet V3. A key focus of our research is the
systematic investigation of data augmentation factors and their impact on model
performance. Through varying the intensity and types of data aug- mentations, such as
rotations, flipping and zooming, we seek to optimize the models’ ability to generalize
from training data to real-world scenarios.
Our findings reveal that precise calibration of data augmentation significantly en hances the diagnostic capabilities of the models. While increased augmentation gen erally improves sensitivity and specificity, excessive augmentation diminishes mod- els’
performances due to overfitting on non-realistic features. As another result, MobileNet
V2 and MobileNet V3 show the highest specificity scores of 0.72 and 0.70, while
EfficientNet V2 demonstrates superior sensitivity of 0.96, indicating the strengths and
trade-offs of different architectures. These results, assessed through a comprehensive set
of metrics including accuracy, sensitivity, specificity, precision, recall, F1 score, and
AUC-ROC, underscore the effectiveness of deep learning meth- ods in COVID-19
identification and the crucial role of tailored data augmentation in improving model
robustness. The implications of our results extend to clinical prac- tice and public health,
highlighting the potential of integrating advanced machine learning technologies into
healthcare workflows to enhance diagnostic efficiency and patient care. Looking ahead,
we propose further exploration into additional imaging modalities, the integration of
multi-modal data, and more sophisticated data aug- mentation techniques, such as usage
of Generative Adversarial Networks (GANs), to refine diagnostic accuracy. Overall, our
study reinforces the significance of transfer
learning and deep learning in addressing the urgent challenges posed by infectious diseases
like COVID-19, paving the way for more sophisticated diagnostic tools.