Abstract:
The diagnosis of diseases through eye images using artificial intelligence (AI)
is a rapidly growing field that has the potential to significantly improve healthcare
outcomes. The eye is a unique and accessible part of the human body that provides
valuable information about a person's overall health. AI algorithms can analyze
images of the eye to identify patterns and features associated with various diseases,
allowing for accurate and non-invasive diagnosis. This thesis proposes the use of AI
in the detection and diagnosis of eye diseases through the analysis of eye images.
The thesis aims to review the current state of the field, including existing AI
algorithms and models used for eye disease diagnosis, as well as their accuracy and
limitations then test them. this allows evaluation of the potential benefits and
challenges of using AI in eye disease diagnosis, such as improved accuracy and
efficiency, reduced cost, and improved access to care, as well as limitations such as
the need for high-quality data and ongoing validation. Moreover, this study provides
develops and tests new AI algorithms and models for eye disease diagnosis,
incorporating innovative approaches such as deep learning and transfer learning to
improve accuracy and handle variations in eye images. the thesis will work to
provide recommendations for the future development and deployment of AI-based
eye disease diagnosis systems, including considerations for data privacy and security,
ethical and legal issues, and the need for ongoing validation and improvement.
Furthermore, the thesis may also contribute to the advancement of knowledge in the
field of AI-based eye disease diagnosis and help to inform the development of new
and more effective methods for detecting and diagnosing eye diseases using AI. the
use of AI in the diagnosis of diseases through eye images holds great potential for
improving healthcare outcomes. However, more research and development are
needed to fully realize the potential of this field.
