This project utilizes a Deep Learning model built with Convolutional Neural Networks (CNN) to predict strokes from CT scans. The goal of this project is to aid in the early detection and intervention of strokes, which can lead to better patient outcomes and potentially save lives.
The model is trained on a dataset of CT scans from Kaggle, which includes both positive (stroke) and negative (no stroke) AKA normal cases.
- https://www.kaggle.com/datasets/noshintasnia/brain-stroke-prediction-ct-scan-image-dataset
- https://www.kaggle.com/datasets/alymaher/brain-stroke-ct-scan-image
The model is a Convolutional Neural Network, a class of deep learning models, that has proven to be highly effective in areas such as image recognition and classification. CNNs are particularly good at picking up on patterns in the input image, such as lines, gradients, circles, or even eyes and faces. It's this property that makes convolutional networks so powerful for computer vision tasks.
Instructions for setting up and installing the project, including any dependencies.
Instructions for using the project, including how to run the model and interpret the results.
We welcome contributions to enhance this project! Feel free to:
- Fork the repository.
- Create a new branch for your improvements.
- Make your changes and commit them.
- Open a pull request to propose your contributions.
We'll review your pull request and provide feedback promptly.
This project is licensed under the MIT License: https://opensource.org/licenses/MIT (see LICENSE.md for details).