Lung Cancer Detection Using Neural Networks

Abstract

The early detection of lung cancer is critical for improving survival rates, yet remains a significant challenge. This project presents an intelligent Lung Cancer Detection System using both Machine Learning and Deep Learning techniques. Built with Streamlit, the platform features a multi-tab interface allowing users to explore datasets, make structured predictions from clinical features using a pickled ML model, and classify lung CT scan images via a Convolutional Neural Network (CNN). The system also highlights prediction confidence and class explanations to improve transparency. Evaluation shows high accuracy and real-time inference, enabling practical usage in screening settings.