COVID-19 Prediction Using Chest X-rays 🧠 Overview This project uses Convolutional Neural Networks (CNNs) to predict COVID-19 infection from chest X-ray images. By analyzing visual patterns indicative of respiratory conditions, the model assists in rapid and accurate detection of COVID-19.

Built using TensorFlow and PyTorch, this solution applies modern deep learning techniques to medical imaging — aiming to support diagnosis in clinical settings.

🚀 Features 📸 Accepts chest X-ray images as input

🧠 CNN-based architecture for accurate classification

🧪 Preprocessing: normalization & data augmentation

🔧 Trained on labeled COVID-19 positive/negative datasets

💻 Frameworks: TensorFlow & PyTorch

🧬 Algorithms Used 🔹 Convolutional Neural Networks (CNNs) CNNs are specially designed for image analysis, capable of learning spatial features like edges, textures, and patterns relevant to lung conditions.

🏗️ Key CNN Layers: Convolutional Layers – Extract feature maps from X-ray inputs

Pooling Layers – Downsample spatial dimensions

Dense (Fully Connected) Layers – Perform classification

🛠 Libraries & Tools Purpose Libraries Used Model Building TensorFlow, PyTorch Image Handling OpenCV, PIL Data Augmentation ImageDataGenerator Evaluation Metrics Scikit-learn

✅ Conclusion This project demonstrates the power of deep learning in medical image analysis. With CNNs, we can effectively classify X-ray images to detect signs of COVID-19.

The model offers strong potential for aiding radiologists and healthcare professionals — especially when rapid, automated diagnosis is needed.

🔮 Future Work 🧠 Transfer Learning: Integrate pre-trained models like ResNet, VGG

🔄 Multi-class Classification: Detect COVID-19, Pneumonia & Normal cases

🌐 Web App Deployment: Real-time prediction with a user interface

📊 Model Optimization: Tune hyperparameters & expand datasets

👨‍💻 Author Bhanu Prakash Achini 📧 bhanuprakashachini08@gmail.com