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Comparison of CNN Architectures for Diagnosing Pneumonia in Chest X-Ray Images
  • Balkaran Singh
Balkaran Singh
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology
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This paper presents, methodical comparisons between four CNN architectures and different learning approaches, for detecting pneumonia in X-Ray images. We evaluate 12 different models obtained by applying three different learning approaches on four different CNN architectures. The results show that transfer learning using fine-tuning performs quite well on all cnn architectures, showing little or no overfitting in most cases. For the overall top model, we find that ResNeXt-50 with fine tuning performs the best. Achieving a high sensitivity (recall) of 98.7%, 75.6% specificity and AUROC of 0.87.