The Tribe

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  Our algorithm accuracy

Inspiration

Lost a close family member due to TB a World Killer diseases according to WHO. The death was caused as a result of long wait to receive lab results as most hospitals have few Radiologists to analyze lab results. I thought of how to prevent that in future and since I study machine learning I develop an Algorithm to help identify patients suffering from TB without the help of a Radiologist the algorithm is Artificial Intelligent.

What it does

Our solution identifies Chest X-ray images of patients suffering from TB in real time. It takes less a minute to analyze the results since its integrated to a Digital Radiography or Magnetic Resonance Imaging (both are technologies used to capture Chest X-ray images). The algorithm has proved to be 90% in interpolating lab results. Currently hospitals can take more than 3 hours to analyze lab results the algorithm bridges that time to less about a minute in analyzing lab results.

How I built it

We have used a Machine Learning Algorithm that uses Computer vision [Convolutional Neural Network (CNN)] to classify the images in real time.

Challenges I ran into

Long run time. It takes more than 1 hour to train the algorithm

Accomplishments that I'm proud of

Proved to 90% in classifying patients that are positive to TB.

What I learned

We can help hospitals become better using AI and we are looking to improve the model to identify diseases such as cancer

What's next for The Tribe

We are currently collecting data to upgrade our algorithm to identify other diseases other than TB like Cancer.

Built With

• api
• github
• python