Developed an AI-based system for automated detection and classification of Diabetic Retinopathy using retinal images. The project focuses on supporting early diagnosis and improving medical decision-making by providing accurate and consistent analysis of eye scans.
The solution was built using deep learning techniques and trained on a large and diverse dataset to ensure reliable performance across different cases and image qualities. It is designed to handle real-world challenges such as data variability and class imbalance, making it suitable for practical healthcare applications.
This project demonstrates the ability to design and implement intelligent AI solutions that address real-world problems, particularly in the medical field, with a strong focus on performance, scalability, and usability.
