Pneumonia is a lung infection mainly caused
by microbes where lungs become inflamed and tiny air
sacs (alveoli) get filled with fluids causing difficulty in
breathing. As stated by the World Health Organization
(WHO), pneumonia is the single largest infectious cause of
death in children worldwide accounting for 15% of all
deaths of children under five years old. While young and
healthy adults have low risk, older people have a greater
chance of having pneumonia and are much more likely to
die from it. The most convenient way to diagnose
pneumonia is through chest x-rays. Deep Learning has
shown some tremendous results in medical image analysis
in recent times. Convolution Neural Networks (CNNs) are
widely used in various classification problems starting
from handwritten digit recognition to self-driving cars.
However, training a CNN model from scratch could be a
tedious task as it requires a huge labeled training data,
extensive computational resources for training the model,
and it often leads to overfitting and convergence issues.
Hence, a convenient alternative for traditional CNN is to
fine-tune a pre-trained CNN that has been trained using a
large dataset. In this paper, we present the performance
analysis of transfer learning and fine-tuning CNN for
classifying pneumonia among the chest x-ray samples.
Our proposed Fine-Tuned CNN model classifies
pneumonia infected chest x-rays into 3 categories
bacterial, normal, and viral achieves an accuracy of
83.33% which is comparable to the performance of
Keywords : Convolution Neural Network, Fine-Tuning, Transfer Learning, Chest X-rays, Medical Imaging.