Lung-Cancer-Detection-Web-App

This project focuses on analyzing different stages of lung cancer using a dataset of 1400 lung cancer images sourced from Kaggle. By leveraging the power of deep learning, we have employed the ResNet-50 architecture with the SGD optimizer to enhance our system's detection capabilities. 💪🔍

Dataset

📚 The dataset utilized in this project consists of 1400 carefully curated lung cancer images obtained from Kaggle. These images form the foundation for training our deep learning model, enabling us to accurately identify and classify different stages of lung cancer. 🖼️

Model Architecture

🧠 To achieve superior performance in lung cancer detection, we have adopted the state-of-the-art ResNet-50 architecture. This deep convolutional neural network (CNN) model is widely recognized for its ability to extract intricate features from medical images, making it an ideal choice for our task. 🏥

Training

🎯 Our ResNet-50 model underwent extensive training using the lung cancer dataset, undergoing multiple iterations and fine-tuning processes. Our primary objective was to maximize the model's accuracy and robustness in classifying various stages of lung cancer, ensuring reliable and precise diagnosis results. 📈

Web Application

🌐 For seamless accessibility, we have integrated the lung cancer analysis system into a user-friendly web application. This application combines the strengths of Django and React, providing users with a dynamic and interactive platform to upload lung cancer images and receive prompt analysis results. 🖥️💻

Technologies Used

🔧⚙️

• Python
• Django
• React
• ResNet-50
• SGD Optimizer

Getting Started

🚀 To set up the Lung Cancer Analysis System locally, follow these steps:

1. Clone this repository.
2. Install the necessary dependencies using pip and npm.
3. Configure your environment to run Django and React applications.
4. Start the Django development server.
5. Start the React development server.
6. Access the web application through your preferred web browser. 🌐🔗