PneuNet

🏥 Chest X-ray Pneumonia Detection using Autoencoder & CNN

This project implements a Convolutional Autoencoder to learn meaningful latent representations from chest X-ray images. A CNN classifier is then trained on the latent features to detect pneumonia.

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📂 Dataset

The dataset consists of chest X-ray images categorized into training, validation, and test sets.

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🚀 Model Architecture

1. Convolutional Autoencoder

• Encoder:

  • 3 Convolutional layers (ReLU activation)
  • Downsamples the input image to a lower-dimensional latent space

• Decoder:

  • 3 Transposed Convolutional layers (ReLU & Sigmoid)
  • Reconstructs the input from the latent space

2. CNN Classifier (on Latent Features)

• Extracts the encoded features from the autoencoder
• Passes them through CNN layers followed by a fully connected layer
• Outputs a single value (binary classification for pneumonia detection)

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