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MalariaDetection AI

Transforming malaria detection with AI precision - empowering healthcare providers to diagnose faster and save lives.

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Inspiration

Malaria remains a significant health issue in many parts of the world, particularly in regions with limited healthcare infrastructure like Kenya. The ability to quickly diagnose malaria is crucial for timely treatment, but the shortage of medical professionals makes this difficult in especially rural areas.

This inspired the creation of Malaria Detection AI, an AI-powered tool designed to help healthcare providers in remote areas by offering fast, accurate malaria parasite detection in blood smear images. The aim is to reduce diagnostic time and make malaria detection accessible, even in the absence of specialized medical expertise.

What It Does

Malaria Detection AI is a web-based tool designed to assist in the diagnosis of malaria by analyzing blood smear images for the presence of malaria parasites. The system leverages AI to quickly and accurately detect parasites, offering healthcare providers in remote areas an efficient diagnostic tool even in the absence of specialized medical professionals.

How It was Built

The platform is built using Laravel 11 with Laravel Breeze for user authentication, providing a secure and easy-to-use interface. The malaria detection capabilities are powered by Gemini Flash AI, a machine learning model designed for the analysis of blood smear images. The data is stored and managed using MySQL for basic storage and Google BigQuery for scalable, secure data management.

Challenges Encountered

  • AI Model Accuracy: One of the primary challenges was ensuring that the AI model could accurately detect malaria parasites in blood smear images, other things like sickle cell, or amoebic shapes kept being confused as parasites.
  • Cloud Integration: Integrating with Google BigQuery for data storage and retrieval presented challenges.
  • Usability: Designing a user interface that is simple yet functional for healthcare professionals with minimal technical expertise was a difficult task, but ultimately essential for the success of the platform.

Accomplishments That I'm Proud Of

  • Successfully integrating Gemini Flash AI to provide fast and accurate malaria detection.
  • Implementing Google BigQuery for efficient and scalable data management, allowing for faster analysis and report generation.
  • Creating a very simple user-friendly interface that caters to the needs of healthcare professionals in rural and resource-limited settings.

What Was Learned

I learned that while AI holds great potential for improving healthcare diagnostics, it is important to focus on usability and accessibility, particularly in remote areas with limited resources. Additionally, integrating cloud technologies like Google BigQuery into our solution allowed us to handle data efficiently and scale the platform as needed.

What's Next for Malaria Detect

Moving forward, the plan is to enhance the platform by adding features such as real-time notifications, improved image processing capabilities, and support for more diagnostic tools. The aim is also to expand the use of the platform to be intergrated into existing healthcare systems or even to do more than just malaria detection and continue to improve the accuracy and performance of the AI model.

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