This project implements a deep learning–based medical image classification system to analyze chest X-ray images and classify them into COVID-19, Pneumonia, or Normal categories. The system uses a Convolutional Neural Network (CNN) and provides a web-based interface for real-time predictions.
Medical image analysis plays a crucial role in assisting healthcare professionals with early diagnosis and decision-making. This project focuses on classifying chest X-ray images using deep learning techniques. A CNN model was trained on a real-world medical dataset to learn visual patterns associated with COVID-19, Pneumonia, and Normal cases.
The trained model is deployed using Streamlit, allowing users to upload chest X-ray images through a web interface and receive instant predictions. The project demonstrates a complete end-to-end machine learning workflow, including data preprocessing, model training, evaluation, deployment, and version control using GitHub.
- Classifies chest X-ray images into:
- COVID-19
- Pneumonia
- Normal
- Real-time prediction through a web interface
- End-to-end deep learning pipeline
- Clean and modular project structure
- Suitable for academic projects and demonstrations
- Dataset Name: Chest X-Ray COVID-19 Pneumonia Dataset
- Source: Kaggle
- Classes: COVID-19, Pneumonia, Normal
- The dataset is pre-split into training and testing folders.
- Dataset is not included in the repository due to size constraints.
- Python
- Deep Learning (CNN)
- PyTorch / TensorFlow
- OpenCV
- Streamlit
- Google Colab
- GitHub