Our Mission
Our project aims to develop a portable, user-friendly device that can capture high-quality fundus images and automatically detect Diabetic Retinopathy (DR) using image processing and machine learning techniques.
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The device will be designed to be accessible, affordable, and easy to operate, enabling widespread screening for DR in various healthcare settings, including resource-limited areas. We seek to improve early detection and management of DR, ultimately reducing the risk of vision loss in patients with diabetes.
We also aim to educate and empower individuals with diabetes to take control of their eye health and prevent complete vision loss. Through our awareness campaigns, we strive to make a positive impact on the lives of those affected by diabetes.
Technology used in prototype
Hardware -
• High-resolution digital camera or smartphone with a high-quality camera module
• Miniaturized optical system for fundus imaging (e.g., lens, filters, illumination)
• Compact and portable housing for the device components
• Embedded processing unit (e.g., single-board computer, microcontroller)
• Display for user interface and image preview
• Battery or power supply for portable operation
• Connectivity options (e.g., USB, Wi-Fi, Bluetooth) for data transfer.
• Storage for captured images and processed data
Software-
• Image processing libraries and frameworks (e.g., OpenCV, scikit-image) learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras)
• Programming languages for development (e.g., Python, C++, Java)
• Data annotation and labeling tools for creating training datasets