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Malaria-Detection

The CNN model employs deep learning and image analysis techniques to automatically extract relevant features from cell images, enabling precise classification. With a high accuracy of around 95%, the model demonstrates its effectiveness in distinguishing between infected and uninfected cells.

The architecture consists of convolutional layers for detecting local patterns, pooling layers for dimension reduction, and fully connected layers for integrating high-level features. Batch normalization is applied to stabilize and improve model performance. After training with a large labeled dataset, the model achieves optimal parameter configuration through backpropagation and gradient descent algorithms.

Documented most parts of the code for easy understanding!

  • Integrate into a web app
  • Train the neural net on multiple diseases

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Malaria detection using CNN

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