Few-Shot Pneumonia Detection: Integrating Siamese Networks with Transfer
Learning for Improved Chest X-Ray Image Classification
Abstract
Pneumonia is a leading cause of death among children under five years of
age (U5s), causing roughly 1.6 million deaths per year. Pneumonia deaths
could be averted if caretakers recognized early the symptoms and danger
signs. Machine learning is a promising technology for the early
detection of pneumonia in children. However, existing models require a
lot of datasets for training, which is scarce, especially in health
settings. Few-shot learning models have been used to address that
challenge but they have issues with computational complexity and are
difficult to train. This study proposes a model that leverages the
strengths of transfer learning and Siamese networks in few-shot
pneumonia detection and classification. Experimental results revealed
that the proposed model had an accuracy of 92.04%, recall of 90.32%,
and F1 score of 90.09%. Also, different values of the learning rate
were recorded and the model performed at its best with the learning rate
of 0.000004. The proposed model also performed well with triplet loss.
The results revealed that the proposed model was robust to overfitting
which is a major limitation with few shot learning models, and was also
easy to train. For future studies, we recommend an ensemble of the
Siamese networks and traditional neural networks such as convolutional
neural networks in the automated detection of pneumonia.