Lung Cancer Detection Using Convolutional Neural Networks

Abstract: Lung cancer is one of the leading causes of death worldwide, where early and accurate detection plays a critical role in improving patient survival rates. This paper presents an automated lung cancer detection system using Convolutional Neural Networks (CNN), a deep learning technique widely used for medical image analysis. The proposed system classifies lung CT scan images into three categories: Benign, Malignant, and Normal. The system incorporates image preprocessing techniques such as resizing, normalization, and noise reduction to enhance model performance. A CNN model is trained to automatically extract features and perform classification without manual intervention. Furthermore, the trained model is deployed using a Flask-based web application, enabling users to upload images and obtain real-time predictions along with confidence scores. Experimental results demonstrate that the proposed system achieves high classification accuracy and significantly reduces dependency on manual diagnosis. The system is efficient, cost-effective, and accessible, making it suitable for preliminary screening and decision support in healthcare environments. This work highlights the potential of deep learning in improving early-stage lung cancer detection and advancing medical diagnostic systems.

Keywords: Lung Cancer Detection, Convolutional Neural Network (CNN), Deep Learning, Medical Image Processing, Flask, Image Classification, Artificial Intelligence in Healthcare