malaria-detection-cnn

Malaria Detection - Custom CNN Model

Model Description

This is a custom Convolutional Neural Network (CNN) trained to detect malaria parasites in cell images. The model classifies blood cell images as either Parasitized or Uninfected.

Model Architecture

• 3 Convolutional layers with ReLU activation
• MaxPooling layers for downsampling
• Dropout layer (0.5) for regularization
• Dense layers for classification
• Binary output with sigmoid activation

Training Details

• Dataset: Cell Images for Detecting Malaria
• Input Size: 150x150x3
• Optimizer: Adam
• Loss Function: Binary Crossentropy
• Epochs: 10
• Validation Split: 20%

Performance

• Validation Accuracy: {history.history['val_accuracy'][-1]:.4f}
• Validation Loss: {history.history['val_loss'][-1]:.4f}