Inspiration
Both of us have been impacted by malaria personally, in the form of its affliction on family members. Because of this, we wanted to utilize machine learning and cutting edge technology such as neural networks to develop a modern solution to this age-old problem.
What it does
Our program takes a picture of a cell or group of cells and analyzes it(s) visual characteristics for signs of infection. With an extremely high accuracy rate of (96%), our program has the potential to make life much easier for doctors as they no longer have to manually examine countless cells under a microscope for miniscule characteristics of malaria.
How we built it
We utilized a neural network to build this machine learning model. This is because neural networks have a high degree of accuracy and are well suited to classification problems, such as this one. The dataset we used is from Kaggle, a data science website with many user-posted datasets. Additionally, we furthered our knowledge of machine learning by attending talks held by HackTheNorth.
Challenges we ran into
We had initially intended to add a UI to make inputting data much easier. However, as this was our first hackathon and we were only a group of 2, implementing a UI proved more difficult than we expected, especially since we would have to use MAMP and other PHP hosting programs.
Accomplishments that we're proud of
We are very excited to build our first project at a hackathon as grade 11 students especially since it was something we were both passionate and interested in. The fact that our model also achieved such a high degree of accuracy further contributed to our feeling of accomplishment.
What we learned
Machine learning modules, principles of data science, and image recognition are just some of the many things we learned. This hackathon has been incredibly useful and definitely made us much better programmers than we were 36 hours ago.
What's next for Malaria Detection System
We hope to implement a UI to make our program much more user-friendly, and also create a mobile application so the program can be used without a PC or laptop. This would prove very useful in developing countries, where the mobility of tools and information is very necessary. On a larger scale, we hope to branch out and create more applications using cutting edge technology to improve health worldwide.
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