Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
This study
investigates the application
of Artificial Intelligence
(AI) techniques, particularly
Convolutional Neural Networks
(CNNs), for the
diagnosis of tympanic
membrane diseases. A
dataset comprising 956
tympanic membrane images
was collected from
various sources, representing
conditions such as
acute otitis media,
chronic otitis media,
foreign body presence,
tympanosclerosis, and other
prevalent disorders affecting
the middle ear
and tympanic membrane.
The images were preprocessed and utilized for training and testing using
deep learning methodologies. Several deep
learning models were
developed and evaluated
on the dataset,
with systematic optimization
of hyperparameters, including
the number of
filters, filter sizes,
and pooling strategies
such as Max
Pooling and Average
Pooling, to enhance
classification performance. The proposed models were assessed based
on a range
of metrics, and
the optimal CNN-based
model achieved an
accuracy of 86.47%
in classifying the
diseases. The findings
of this study
underscore the potential
of AI-driven solutions
to improve medical
diagnostics by offering
reliable and efficient
tools to assist
healthcare professionals in the accurate
identification of tympanic
membrane diseases. Future work
is recommended to
explore advanced deep
learning architectures and
expand the dataset
to further enhance
accuracy and generalizability across
diverse medical applications.
Country : Iraq
IRJIET, Volume 9, Issue 5, May 2025 pp. 42-50