ABSTRACT
Pneumonia ranks as a leading cause of mortality, particularly in children
aged five and under. Detecting this disease typically requires radiologists to
examine chest X-rays and report their findings to physicians, a task
susceptible to human error. The application of Deep Transfer Learning (DTL) for
the identification of pneumonia through chest X-rays is hindered by a shortage
of available images, which has led to less than optimal DTL performance and
issues with overfitting. Overfitting is characterized by a model’s learning
that is too closely fitted to the training data, reducing its effectiveness on
unseen data. The problem of overfitting is especially prevalent in medical
image processing due to the high costs and extensive time required for image
annotation, as well as the challenge of collecting substantial datasets that
also respect patient privacy concerning infectious diseases such as pneumonia.
To mitigate these challenges, this paper introduces the use of conditional
generative adversarial networks (CGAN) to enrich the pneumonia dataset with
2690 synthesized X-ray images of the minority class, aiming to even out the
dataset distribution for improved diagnostic performance. Subsequently, we
applied four modified lightweight deep transfer learning models such as
Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been
fine-tuned and evaluated, demonstrating remarkable detection accuracies of
99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The
experimental results validate that the models we have proposed achieve high
detection accuracy rates, with the best model reaching up to 99.26%
effectiveness, outperforming other models in the diagnosis of pneumonia from
X-ray images.