Authors :
T. Jayasri; Yakkala Prudhvi Raj; Maartha Harshitha; Pallapati Raghavendra Rao; Devireddy Sai Krshina Akhil
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/vumu9e7d
Scribd :
https://tinyurl.com/2jvbjba8
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR704
Abstract :
The project explores the deployment of
Convolutional Neural Networks (CNN) and the
Inception V3 model for the automated detection and
classification of diabetic retinopathy stages using fundus
images. Recognizing diabetic retinopathy as a leading
cause of blindness among the working-age population
globally, this research aims to streamline the diagnostic
process, traditionally reliant on the manual examination
by ophthalmologists. Through the utilization of the
DRIVE and STARE datasets, the project benchmarks
the performance of CNN and Inception V3 models in
accurately categorizing the severity of diabetic
retinopathy into five distinct stages. The comparison
between these models is grounded on parameters such
as accuracy, loss, and predicted value, with findings
indicating Inception V3's superiority in both
performance metrics and diagnostic precision. This
advancement could significantly contribute to early and
more accessible detection of diabetic retinopathy,
thereby mitigating progression towards blindness.
Furthermore, the project underscores the potential of
deep learning algorithms in enhancing diagnostic
methodologies for retinal diseases, paving the way for
future explorations in the field of medical imaging and
artificial intelligence.
Keywords :
Diabetic Retinopathy, Convolutional Neural Networks, Inception V3, Fundus Images, Automated Detection.
The project explores the deployment of
Convolutional Neural Networks (CNN) and the
Inception V3 model for the automated detection and
classification of diabetic retinopathy stages using fundus
images. Recognizing diabetic retinopathy as a leading
cause of blindness among the working-age population
globally, this research aims to streamline the diagnostic
process, traditionally reliant on the manual examination
by ophthalmologists. Through the utilization of the
DRIVE and STARE datasets, the project benchmarks
the performance of CNN and Inception V3 models in
accurately categorizing the severity of diabetic
retinopathy into five distinct stages. The comparison
between these models is grounded on parameters such
as accuracy, loss, and predicted value, with findings
indicating Inception V3's superiority in both
performance metrics and diagnostic precision. This
advancement could significantly contribute to early and
more accessible detection of diabetic retinopathy,
thereby mitigating progression towards blindness.
Furthermore, the project underscores the potential of
deep learning algorithms in enhancing diagnostic
methodologies for retinal diseases, paving the way for
future explorations in the field of medical imaging and
artificial intelligence.
Keywords :
Diabetic Retinopathy, Convolutional Neural Networks, Inception V3, Fundus Images, Automated Detection.
