loading page

Improving Pneumonia Diagnosis with RadImageNet: A Deep Transfer Learning Approach
  • Arzu Ahmadova,
  • Ismail Huseynov,
  • Yusif Ibrahimov
Arzu Ahmadova
Faculty of Mathematics, University of Duisburg-Essen

Corresponding Author:[email protected]

Author Profile
Ismail Huseynov
Faculty of Mathematics and Computer Science, University of M√ľnster
Yusif Ibrahimov
Department of Computer Science, University of York


Pneumonia, a lung infection causing inflammation, remains a leading cause of global mortality, resulting in an estimated annual fatality rate of approximately 4 million individuals according to the World Health Organization. Accurate and timely diagnosis of pneumonia in its early stages is crucial for effective patient care. Given the criticality of this issue, our study introduces an automated approach utilizing convolutional neural networks and transfer learning to detect pneumonia in chest X-ray images of children aged 1 to 5 years. We employed two pretrained ResNet-50 models trained on different datasets: ImageNet, containing 14 million natural images, and RadImageNet, consisting of 1.4 million medical images. Our results show that RadImageNet outperformed ImageNet, demonstrating superior performance in pneumonia detection. Evaluation using six key performance metrics revealed that the ResNet-50 model pretrained on RadImageNet achieved superior performance compared to the ImageNet-based model, further emphasizing the efficacy and interpretability of RadImageNet for binary classification tasks in medical image datasets. These findings underscore the importance of RadImageNet as a valuable source of pretrained models, particularly for small medical image datasets. 1