🩺 Pneumonia Detection

Deep learning model for detecting pneumonia from chest X-ray images using DenseNet121 transfer learning.

Accuracy: 91.7% | Pneumonia Detection: 96.9%

📥 Dataset

Download the chest X-ray dataset from Kaggle:

https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia

Extract to chest_xray/ folder in project directory.

🚀 Quick Start

1. Install requirements

pip install -r requirements.txt

2. Download and extract dataset to chest_xray/ folder

3. Run model

python main.py

🔄 Options

View Results: python main.py - Shows model performance and confusion matrix

Train New Model: python train_model.py - Trains from scratch (takes time)

📊 Model Architecture

• Transfer Learning: DenseNet121 (pre-trained on ImageNet)
• Custom Layers: GlobalAveragePooling2D + Dense layers
• Training: Two-phase approach (classifier training + fine-tuning)

📈 Results

🔬 Research Note: These results can be further improved! With longer training (increased epochs), different optimization techniques (learning rate scheduling, ensemble methods) and advanced data augmentation strategies, 95%+ accuracy is achievable.

📁 Files

• main.py - Show results
• train_model.py - Train new model
• config.py - Configuration settings
• best_model.h5 - Pre-trained model

🛠️ Requirements

• Python 3.8+
• TensorFlow 2.x
• See requirements.txt for full list

⚕️ Medical Disclaimer

This project is for research/educational purposes only. Not intended for actual medical diagnosis.