CovidSense AI

Inspiration

Our inspiration stems from the urgent need for efficient and cheap COVID-19 detection methods especially for marginalized communities, driving us to explore innovative approaches using AI technology.

What it does

CovidSense AI utilizes advanced machine learning techniques to analyze cough sounds, offering a non-invasive and rapid method for COVID-19 detection with high accuracy.

How we built it

We built CovidSense AI by leveraging state-of-the-art CRNN (Convolutional Recurrent Neural Network) with attention mechanisms, meticulously training and fine-tuning the model on a diverse dataset of cough sounds.

Challenges we ran into

Throughout the development process, we encountered challenges in dataset acquisition, model optimization, and balancing accuracy with real-world applicability.

Accomplishments that we're proud of

We are proud to have developed a robust AI model capable of accurately detecting COVID-19 from cough sounds, contributing to the global effort to combat the fight against COVID.

What we learned

Through this project, we gained valuable insights into machine learning model development, dataset preprocessing, and the importance of collaboration in addressing global health challenges.

What's next for CovidSense AI

In the future, we aim to further refine and validate CovidSense AI through clinical trials and real-world deployment, with the ultimate goal of providing accessible and scalable COVID-19 screening solutions.

Built With

• keras
• python
• tensorflow