Malaria Detection From Red Blood Cells Picture

Inspiration

Malaria is one of the most fatal diseases in the world and many third world countries don't have access to methods of identifying and treating Malaria.

What it does

Our web application receives an image of blood cells and determines if the patient has malaria and refers them to useful apps for proper medical procedures.

How we built it

We used a machine learning model and trained it with a malaria blood cell dataset and created the web app using stream lit.

Challenges we ran into

One challenge we ran into was an issue with downloading the necessary libraries and software issues. Furthermore, determining the model to use was also another challenge. We had trouble between using a support vector machine or convolutional neural network but decided to use the support vector machine due to its simplicity and limited time.

Accomplishments that we're proud of

We are extremely proud of creating the model and being able to implement it in a web application that is user friendly and easy to understand.

What we learned

One thing we learned is that machine learning projects have many technical aspects that have to be set up even before writing our first lines of code.

What's next for Malaria Detection From Red Blood Cells Picture

We plan to improve on our front end and allow the user to gain more access to knowledge and resources about malaria. Furthermore, we could alter our model to a CNN to make it more efficient as we are not time constrained.

Built With

• python
• streamlit