Preprocessing of CT Scans for Lung Cancer Detection

Lung cancer, a pervasive global health concern, requires effective early detection and classification methodologies to improve patient outcomes. This project addresses this challenge by developing a comprehensive framework utilizing medical imaging data for the timely classification of lung cancer with better results of diagnosis. Leveraging advanced processing techniques and DL algorithms, the project aims to make the lung cancer detection system more efficient. By preprocessing DICOM image datasets of lung scans and employing techniques such as noise reduction, contrast enhancement, and feature extraction, the project seeks to improve image quality and extract relevant features.