We imagine a world where no one dies from a bite of mosquito. In some areas of the world, mosquitoes are more than a nuisance, causing more than 4 millions deaths annually. To help fight against Malaria, we are building a weapon/tool that allows them to detect malaria quicker and accurately.

What it does

Our tool will give the prediction/probability for whether a patient is infected with malaria or not, given the input images from the patient's blood samples in browser.

How we built it

Our tool was built using neural network, machine learning, react, flask, javascript, css, html and python. In addition, we have trained a machine learning classification model that working appropriately with F1 score of 0.96.

Challenges we ran into

There are many challenges we ran into. We were using some technologies that we had never used before. We worked non-stop to get everything hooked up within 24 hours.

Accomplishments that we're proud of

Trained a powerful machine learning model, utilizing open source data from U.S National Library of Medicine. Learned new technologies and make them worked properly.

What we learned

We learned a mindset that it is possible to make your dream project come true within 24 hours if you have a good teamwork.

What's next for project_m

Making mobile applications for iOS and Android platforms.

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