X-Ray Visionaries

Inspiration

The project was inspired by the need for efficient, accurate pneumonia detection in healthcare, especially in settings where timely diagnostics can make a crucial difference. We wanted to leverage AI to support radiologists by automating image analysis, ultimately improving patient outcomes.

What it does

Our model detects pneumonia in chest X-ray images, providing a preliminary diagnostic that can support radiologists in identifying high-risk cases quickly and accurately.

How we built it

We built the model using a CNN-based feature extraction approach, which provided a strong foundation for detecting pneumonia patterns in X-ray images. We developed and trained the model using a labeled dataset, fine-tuning hyperparameters to achieve a reliable baseline. Our goal is to enhance this model with transformers for even more precise pattern recognition.

Challenges we ran into

One challenge was managing the limited time to experiment with advanced techniques like transformers, which can improve model accuracy by capturing complex image features. Additionally, handling data preprocessing for optimal image quality and accuracy was essential, requiring careful tuning.

Accomplishments that we're proud of

We’re proud of developing a working, reliable baseline model under time constraints and setting up the foundations to integrate transformers in the future, which could elevate our model’s diagnostic power.

What we learned

We gained valuable experience in using CNNs for medical image classification and learned how to handle data preprocessing in radiology contexts. We also explored the potential of transformers, which will be instrumental for future improvements.

What's next for X-Ray Visionaries

The next step is integrating transformers to improve accuracy by capturing deeper image features. We’re also looking to expand the dataset and refine model parameters, ultimately aiming to deploy our model for real-world testing and potential clinical use.

Built With

• colab
• jupyter