xan-x / malaria-classification

A Deep learning model that helps in prediction of cells diagnose with Malaria

Malaria-Classification

A Deep learning model that helps in prediction of cells diagnose with Malaria

Malaria Classification Project

Overview

This Jupyter Notebook project focuses on using deep convolutional neural networks (CNNs) to classify cells and predict whether they are diagnosed with malaria. Achieving an accuracy of 93%, the model demonstrates its efficacy in automated diagnosis.

Files

• Malaria_Detection.ipynb: The Jupyter Notebook containing the code for the Malaria Classification project.

Data

The dataset used for training and evaluation is the Malaria Dataset from Kaggle, consisting of infected and uninfected cell images.

Samples

• 0 : Infected
• 1 : Un-Infected

Architecture

Results

The trained model achieved an accuracy of 93% on the test dataset. For detailed performance metrics and visualizations, refer to the outputs in the notebook.

Loss

Accurracy

Contributing

Contributions are welcome! If you find any issues or want to enhance the project, please open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Conclusion

Thank you for exploring the Malaria Classification project! I hope this project inspires and contributes to advancements in automated diagnosis using deep learning techniques. Feel free to explore the code, experiment with the notebook, and contribute to the project. If you have any questions, suggestions, or feedback, please don't hesitate to reach out.

Happy coding and stay curious!

😀