🫁 Pneumonia Detection using DenseNet

A deep learning-based system for detecting pneumonia from chest X-ray images using the DenseNet convolutional neural network architecture.

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📌 Overview

Pneumonia Detection using DenseNet is a medical imaging project that applies deep learning and convolutional neural networks (CNNs) to automatically classify chest X-ray images as Pneumonia or Normal.

The project focuses on leveraging the DenseNet architecture, which improves feature reuse and gradient flow, making it well-suited for medical image analysis.

The methodology, design, and results are documented in the included PDF report.

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✨ What This Project Does

• 🩻 Analyzes chest X-ray images
• 🧠 Uses a pre-trained DenseNet CNN model
• 🔍 Extracts deep features for classification
• 📊 Predicts whether pneumonia is present
• 📓 Demonstrates model training, evaluation, and inference

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🧰 Technology Stack

• Python
• Jupyter Notebook
• TensorFlow / Keras
• DenseNet (CNN Architecture)
• NumPy, Matplotlib
• Medical Image Dataset (Chest X-rays)

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🎯 Applications

• Medical image analysis
• Clinical decision support systems
• AI-assisted diagnosis
• Healthcare and biomedical research
• Deep learning education

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⭐ What this Project is about:

• Addresses a real-world healthcare problem
• Applies state-of-the-art CNN architecture (DenseNet)
• Demonstrates end-to-end ML workflow

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📌 Notes

• Model performance depends on dataset quality and size
• GPU acceleration is recommended for training