---
license: mit
metrics:
- accuracy
library_name: keras
---

# 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}