Malaria Detection with LeNet

This project focuses on blood classification for malaria using the Tensorflow.Datasets function in Python.

Libraries

This project makes use of several Python libraries:

• TensorFlow: An open-source machine learning library developed by Google.
• NumPy: A library for efficient manipulation of multi-dimensional arrays.
• Matplotlib: A library for data visualization and plotting.
• TensorFlow Datasets (tensorflow_datasets): A TensorFlow extension for accessing and working with datasets.
• Google Colab (for drive): A platform that provides free access to GPU and TPU resources for machine learning.
• Keras: A high-level neural networks API running on top of TensorFlow.
• Other Keras Components: Various Keras components, including layers (Conv2D, MaxPool2D, Dense, Flatten, BatchNormalization, etc.), loss functions (BinaryCrossentropy), metrics (MeanSquaredError, Accuracy, BinaryAccuracy, etc.), optimizers (Adam), and callbacks (Callback, ModelCheckpoint, EarlyStopping, TensorBoard, ReduceLROnPlateau).
• ImageDataGenerator (from tensorflow.keras.preprocessing.image): A utility for augmenting and preprocessing image data.

Model

The model used for classification is LeNet, implemented using the Sequential API of TensorFlow. LeNet is a convolutional neural network (CNN) developed by Yann LeCun in the 1990s and is widely used for image classification tasks.

Usage

To run this project:

1. Ensure you have all the listed libraries installed.
2. Execute the provided source code in the repository.
3. Set up your working environment and make sure you have access to the malaria image dataset.
4. Run the code to train the model and test it on the test data.

Planned Improvements

Here are some planned improvements for this project:

1. Data Augmentation: Implement more advanced data augmentation techniques to improve model generalization.

2. Hyperparameter Tuning: Optimize the model's hyperparameters to achieve better performance.

3. Deployment: Create a user-friendly interface for malaria detection and deploy the model as a web application or mobile app.

4. Model Interpretability: Explore techniques for explaining the model's predictions, making it more transparent and interpretable.

5. More Datasets: Include additional datasets for a broader range of malaria images, increasing the model's versatility.

Contributions

If you wish to contribute to this project or have suggestions to improve it, please open an issue or a pull request.

Thank you for your interest in "Malaria Detection with LeNet"!