X_ray ML model

Inspiration:

My husband is studying medicine and he constantly shares what he is learning and the diagnosis however I understand very little. So, I have been thinking about the tools that could be useful for doctors and that could be applied using computer vision.

What it does:

It classifies x-rays into 4 categories, pneumonia, COVID-19, tuberculosis, and normal, using a neural network.

What tools did I use to build it:

I built this ML model using Python and various libraries, such as numpy, pandas, Tensorflow, IPython, and sklearn

What didn't work:

I wanted to implement this ML model as an iOS app and found open-source code with a similar ML model however I wasn't able to implement it as an ML model file to create the app. I also had a very hard time training the data because it kept getting all sorts of problems

What was challenging:

This was my first time going solo and I was very proud of how much I was able to complete since I couldn't rely on teammates for help and had to figure it all out on my own.

What we learned: I learned that developing the model is just part of the process but it is essential to be able to test it with the training and validation to ensure its accuracy. Learning how to deploy the ML model also will enable it to give it real-world use.

What's next for the X_ray ML model: I figured out that I will need to learn Swift to be able to develop an iOS app and how to use the model to incorporate it.

Built With

• python
• sklearn
• tensorflow