Abstract:
Tiny air sacs in one or both lungs become inflamed
as a result of the lung infection known as pneumonia. In order to
provide the best possible treatment plan, pneumonia must be
accurately and quickly diagnosed at initial stages. Nowadays, a
chest X-ray is regarded as the most effective imaging technique
for detecting pneumonia. However, performing chest X-ray
analysis may be quite difficult and laborious. For this purpose, in
this study we propose deep convolutional neural network (CNN)
with 24 hidden layers to identify pneumonia using chest X-ray
images. In order to get high accuracy of the proposed deep CNN
we applied an image processing method as well as rescaling and
data augmentation methods as shear_range, rotation, zooming,
CLAHE, and vertical_flip. The proposed approach has been
evaluated using different evaluation criteria and has demonstrated 97.2%, 97.1%, 97.43%, 96%, 98.8% performance in terms of
accuracy, precision, recall, F-score, and AUC-ROC curve. Thus,
the applied deep CNN obtain a high level of performance in
pneumonia detection. In general, the provided approach is
intended to aid radiologists in making an accurate pneumonia
diagnosis. Additionally, our suggested models could be helpful in
the early detection of other chest-related illnesses such as
COVID-19.
