A deep learning model that analyzes chest X-ray images to distinguish between COVID-19 infected lungs and normal lungs. Built using Convolutional Neural Networks (CNN) for rapid, accurate respiratory disease screening.

LungScanAI is a deep learning project that uses Convolutional Neural Networks (CNNs) to classify chest X-ray (CXR) images as either COVID-19 infected lungs or normal lungs. This tool aims to support rapid respiratory screening, especially in resource-limited settings.

• Trained a CNN model on publicly available COVID-19 CXR datasets
• Performed image preprocessing, data augmentation, and binary classification
• Evaluated model performance using accuracy

• Convolutional Layers with ReLU activations

• Max Pooling for feature reduction

• Dense Layer(s) with Dropout for regularization

• Sigmoid Output for binary classification

  ✅ Pretrained Model (Transfer Learning)

• MobileNetV2 with custom classification head

• Fine-tuned for COVID-vs-Normal classification

• Python
• TensorFlow / Keras
• NumPy / Pandas
• Matplotlib / Seaborn

The dataset includes labeled COVID-19 and Normal lung chest X-ray images collected from open public sources.

• Accuracy
• Precision / Recall