varun30300/Malaria-Infected-cell-detection-using-CNN

This CNN model will take a cell image as an input and then it will classify as one of the two classes :- Parasatic or healthy

Cell Image Classification: Parasitic vs. Uninfected

This Convolutional Neural Network (CNN) model takes a cell image as input and classifies it as either Parasitic or Uninfected. The dataset used for training and testing is sourced from Kaggle.

Dataset

Download the dataset from Kaggle.

Steps to Use This Model

1. Download the Source Code

   Download the source code and place it in a directory.

2. Download and Unzip the Dataset

   Download the dataset from the provided link and unzip it in the same directory as the source code.

3. Organize the Dataset

   The unzipped folder contains two subfolders:

   • Parasitized
   • Uninfected

   Rename the unzipped folder to Train.

4. Create Test Folders

   Create a new folder named Test and within it, create two subfolders:

   • Parasitized
   • Uninfected

5. Prepare the Test Dataset

   • Move 300 images from Train/Parasitized to Test/Parasitized.
   • Move 300 images from Train/Uninfected to Test/Uninfected.

6. Update the Paths in the Model

   Ensure the paths to the training and testing datasets are correctly updated in the model code.

Running the Model

Once you have organized the dataset and updated the paths in the model code, you can run the model script to train and test the CNN model on the cell images.