This project involves the development of a web application that leverages machine learning for retina blood vessel segmentation. It uses a deep learning model trained on medical retina images to segment the blood vessels, aiding in the diagnosis of retinal diseases. The application is built using FastAPI and integrates with a pre-trained U-Net model for image segmentation.

Key Features:

Image Upload: Users can upload retina images for segmentation through a simple and user-friendly form.

Segmentation Prediction: Once the image is uploaded, the application processes the image using a deep learning model (U-Net) to segment the blood vessels in the retina.

Result Display: The segmented result is displayed alongside the original image, both resized to 128x128 pixels for consistent viewing.

Backend Model: The project uses a TensorFlow-based deep learning model (unet_model.h5) that has been trained to accurately segment blood vessels from retina images.

Image Preprocessing: Uploaded images are resized, converted to grayscale, and equalized to improve model performance during prediction.

Prediction Mask: The output of the model is a binary mask that highlights the blood vessels, and it is visualized as a separate image.

Base64 Encoding: The original image and prediction result are encoded in Base64 format and displayed directly in the browser.

Technology Stack: Backend: FastAPI for building the web application and handling image uploads.

Machine Learning: TensorFlow and Keras to run the pre-trained U-Net model for segmentation.

Frontend: HTML, CSS, and JavaScript for creating the user interface, handling file uploads, and displaying results.

Image Processing: OpenCV and PIL for preprocessing the images before feeding them into the model.

How it Works: User Uploads Image: The user uploads an image via the web interface. Image Processing: The image is resized to 128x128 pixels and processed using the U-Net model. Prediction: The model predicts the segmentation mask for the retina blood vessels. Display Results: The application returns and displays both the original resized image and the prediction mask.

Use Case: This tool is particularly useful for medical professionals in the field of ophthalmology, who can use it to assist in detecting retinal diseases like diabetic retinopathy, glaucoma, and other conditions that affect the blood vessels of the retina. The tool offers a quick, automated way to highlight important features within retina images, improving the speed and accuracy of diagnoses.

Next Steps / Enhancements: Model Improvement: Continue to improve the U-Net model with more data and fine-tuning to enhance accuracy. Batch Processing: Allow batch uploads and predictions to process multiple images simultaneously. Error Handling: Add more robust error handling to ensure the application is fault-tolerant. User Authentication: Implement user authentication for privacy and security, allowing users to store and track results. This project serves as a stepping stone towards creating powerful diagnostic tools that leverage machine learning for medical image analysis, providing valuable insights to healthcare professionals.