arham-kk / malaria-detection-models

This repository contains code for a malaria detection system using a pre-trained ResNet50 model on TensorFlow. The model is trained to detect malaria parasites in cell images.

Malaria Detection Models

This repository contains three Jupyter Notebook files (ipynb) for malaria detection models using VGG-16, VGG-19, and ResNet-50 architectures. These models are implemented using Google Colab.

Models

1. VGG-16
2. VGG-19
3. ResNet-50

Usage

To run these notebooks, follow the steps below:

1. Clone this repository to your local machine using the following command:

git clone https://github.com/arham-kk/malaria-detection-models.git

2. Open Google Colab (colab.research.google.com).
3. Upload the desired .ipynb file(s) to your Google Colab workspace.
4. Ensure that you have the necessary dataset for malaria detection.
5. Modify the notebook code if necessary, such as updating file paths or adjusting hyperparameters.
6. Execute the notebook cells sequentially to train the model and perform malaria detection.
7. Analyze the results and evaluate the model's performance.

Ensure that these dependencies are installed in your Python environment before running the notebooks.

Acknowledgments

1. The dataset used in this project was obtained from the Malaria Cell Images Dataset on Kaggle.
2. The ResNet50 model used in this project was pre-trained on the ImageNet dataset.