Pneumonia Detection in Chest X-Ray Images using ResNet50V2

🧠 Project Summary

This project addresses the classification of chest X-ray images to detect signs of pneumonia, a common and serious complication observed during the COVID-19 pandemic.

Leveraging the power of Artificial Intelligence and Deep Learning, the study uses the ResNet50V2 architecture—pretrained on large image datasets—to classify X-ray images into two categories: with pneumonia and without pneumonia.

The project highlights the importance of medical image analysis in times of health crises, aiming to support overwhelmed healthcare systems with fast, automated diagnostics.

📊 Objectives

• Preprocess a dataset of chest X-ray images
• Apply transfer learning using the ResNet50V2 convolutional neural network
• Evaluate model performance to optimize classification results

🧰 Tools & Libraries

• Python
• TensorFlow
• Keras
• PyTorch
• NumPy, Pandas, Matplotlib (for data handling and visualization)

📁 Dataset

• Chest X-ray images labeled as:
  • Pneumonia
  • Normal
• Publicly available datasets such as the Chest X-Ray Images (Pneumonia) dataset on Kaggle were used.

🔍 Workflow

1. Data Preprocessing
   • Image resizing and normalization
   • Train-test split
2. Modeling
   • Load pretrained ResNet50V2
   • Add custom classification layers
   • Fine-tune with transfer learning
3. Evaluation
   • Accuracy, precision, recall, and confusion matrix
   • Optimization of hyperparameters