kb22/Malaria-Cell-Identifier-using-CNN

This project trains a Convolutional Neural Network (CNN) using Keras that can identify Malaria cells with a test accuracy of more than 96%.

Malaria-Cell-Identifier-using-CNN

The project deals with using Convolutional Neural Network (CNN) in Keras to identify Malaria Cell images. The data is taken from Kaggle: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria

Get the dataset

1. Download the dataset from the website: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria
2. Extract the .zip file and a folder will appear cell_images
3. Copy the folder cell_images to the directory where the project is located (the folder Malaria-Cell-Identifier-using-CNN) as described below

Usage (With virtualenv environment)

1. Clone the repository to your machine when inside a given directory

git clone https://github.com/kb22/Malaria-Cell-Identifier-using-CNN.git

2. Create a virtual environment

virtualenv -p python3 Malaria-Cell-Identifier-using-CNN

3. Go inside the folder Malaria-Cell-Identifier-using-CNN

cd Malaria-Cell-Identifier-using-CNN

4. Activate the environment

source bin/activate

5. Install all dependencies

pip install -r requirements.txt

6. Run the notebook

jupyter notebook

Usage (Without environment)

1. Clone the repository to your machine when inside a given directory

git clone https://github.com/kb22/Malaria-Cell-Identifier-using-CNN.git

2. Go inside the folder Malaria-Cell-Identifier-using-CNN

cd Malaria-Cell-Identifier-using-CNN

3. Install all dependencies

pip install -r requirements.txt

4. Run the notebook

jupyter notebook