Authors :
Dr. J. Jeyaboopathiraja; Tamilarasan R
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/3vzke5m4
Scribd :
https://tinyurl.com/2zswjx3x
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1859
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Pneumonia is a known potentially fatal lung
disease that is frequently referred to as a silent killer
since it can lead to lung alveoli filling with pus or fluid,
mainly from fungal, viral, or bacterial infections. Chest
X-rays are the primary diagnostic tool for pneumonia;
however, the diagnosis becomes more complex when
other pulmonary disorders such volume loss,
haemorrhage, lung cancer, fluid overload, and
consequences from radiation or surgery are taken into
account. As a result, the interpretation of chest X-rays
becomes complex, which makes the development of
computer-aided diagnosis systems necessary to help
physicians make decisions that are more accurate. In
order to diagnose pneumonia from chest X-ray pictures,
the research reported here uses a convolutional neural
network (CNN) enhanced with a self-attention
mechanism. 'Normal' and 'pneumonia' classes are
included in the dataset used in the study methodology,
and data augmentation techniques are applied to
improve the model's resilience. By means of extensive
evaluation metrics and visualizations, the study
highlights the potential of the suggested model as a
useful instrument to aid clinicians in diagnosing
pneumonia, consequently reducing the difficulties linked
to the interpretation of chest X-rays in the context of
various pulmonary conditions.
Keywords :
X-rays, Pneumonia, Normal, CNN, Self Attention.
Pneumonia is a known potentially fatal lung
disease that is frequently referred to as a silent killer
since it can lead to lung alveoli filling with pus or fluid,
mainly from fungal, viral, or bacterial infections. Chest
X-rays are the primary diagnostic tool for pneumonia;
however, the diagnosis becomes more complex when
other pulmonary disorders such volume loss,
haemorrhage, lung cancer, fluid overload, and
consequences from radiation or surgery are taken into
account. As a result, the interpretation of chest X-rays
becomes complex, which makes the development of
computer-aided diagnosis systems necessary to help
physicians make decisions that are more accurate. In
order to diagnose pneumonia from chest X-ray pictures,
the research reported here uses a convolutional neural
network (CNN) enhanced with a self-attention
mechanism. 'Normal' and 'pneumonia' classes are
included in the dataset used in the study methodology,
and data augmentation techniques are applied to
improve the model's resilience. By means of extensive
evaluation metrics and visualizations, the study
highlights the potential of the suggested model as a
useful instrument to aid clinicians in diagnosing
pneumonia, consequently reducing the difficulties linked
to the interpretation of chest X-rays in the context of
various pulmonary conditions.
Keywords :
X-rays, Pneumonia, Normal, CNN, Self Attention.
