Abstract:
This article examines a current area of research in the field of ophthalmology
the use of deep learning methods for automated analysis of retinal structures.
This work explores the use of deep learning methods such as EfficientNet
and DenseNet for the automated analysis of retinal structures in
ophthalmology. EfficientNet, originally proposed to balance between
accuracy and computational efficiency, and DenseNet, based on dense
connections between layers, are considered as tools for identifying and
classifying retina features. Automated analysis includes identifying
pathologies, assessing the degree of their development and, possibly,
diagnosing various eye diseases. Experiments are performed on a dataset
containing a variety of images of retinal structures. Results are evaluated
using metrics of accuracy, sensitivity, and specificity. It is expected that the
proposed deep learning methods can significantly improve the automated
analysis of retinal images, which is important for the diagnosis and
monitoring of eye diseases. As a result, the article highlights the significance
and promise of using deep learning methods in ophthalmology for
automated analysis of retinal structures. These methods help improve the
early diagnosis, treatment and monitoring of eye diseases, which can
ultimately lead to improved healthcare quality and improved patient lives
