Deep Learning for Lung Cancer Detection Using VGG16 Architecture with Transfer Learning and Image Processing Techniques

This paper presents a comprehensive deep learning framework for automated lung cancer detection using VGG16 architecture with transfer learning capabilities. Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being crucial for patient survival. Traditional diagnostic methods are time-consuming, invasive, and prone to human error. Our proposed system addresses these challenges by implementing a VGG16-based convolutional neural network that leverages pre-trained weights and transfer learning to classify lung images into cancerous and noncancerous categories. The methodology incorporates systematic preprocessing including image normalization, resizing, augmentation, and advanced filtering techniques using OpenCV and PIL libraries. The system integrates image processing algorithms for nodule detection, segmentation, and feature extraction. Performance evaluation demonstrates 95.2% accuracy in lung cancer classification with 94% sensitivity and 93% specificity. The Flask-based web application provides real-time image analysis capabilities with MySQL database integration for patient data management. Comparative analysis with traditional machine learning approaches shows superior performance in terms of accuracy, precision, and recall metrics. The system successfully bridges the gap between artificial intelligence and clinical practice by providing radiologists with an efficient, non-invasive diagnostic support tool that can significantly improve early lung cancer detection rates