Approach

To address the complex requirements, we adopted a systematic approach:

Image Capture: We integrated a user-friendly interface, allowing operators to input retinal images effortlessly.

Image Processing: Python and Computer Vision techniques were used to preprocess and enhance retinal images, preparing them for analysis.

Diagnosis Models: EfficientNet, ResNet, and VGG models were employed to analyze the retinal images and classify the severity of diabetic retinopathy.

Reporting and Recommendation: The application generated detailed reports and offered treatment recommendations based on the diagnosis, ensuring clarity for both medical professionals and patients.

User Interface: PyQT was used to create an intuitive and user-friendly interface for operators, making the entire process seamless.

Technologies

Outcome

The project achieved remarkable results:

Efficient Diagnosis: The application accurately diagnosed diabetic retinopathy, allowing for early intervention and treatment.

Detailed Reports: Comprehensive reports were generated automatically, aiding medical professionals in decision-making.

Treatment Recommendations: The system provided clear course-of-action recommendations, ensuring patients received appropriate care.

Streamlined Workflow: The automation of diagnosis and reporting significantly reduced the workload on ophthalmologists and medical staff.