HaemaScan – Visual Anaemia Detection System
Exploring AI-powered visual analysis for preliminary anaemia screening.
HaemaScan is a machine-learning and computer-vision project exploring visual anaemia detection through an interactive web application. It aims to provide a technology-assisted preliminary screening workflow by connecting visual input, image analysis, and prediction functionality through a Flask-based interface.
Technology stack
Problem
Conventional anaemia screening relies on laboratory blood testing. This project explores whether visual information can support an accessible preliminary screening approach through machine learning and image analysis.
Approach
Developed a Python-based application using Flask to integrate the visual analysis and prediction workflow with a user-facing web interface. Organized the application around image processing, model inference, and presentation of prediction results.
Solution
Created a web-based visual anaemia detection prototype that connects image-based analysis with a Flask application.
Results
Implemented an academic research prototype demonstrating the integration of computer vision, machine learning, and web application development. Model accuracy, sensitivity, specificity, and other evaluation metrics should be added after verifying the actual experimental results.