A Survey on Optimized CT Scan Analysis for Lung Cancer Detection Using Convolutional Neural Networks

Lung cancer is a major cause of death around the world, so finding it early can help people get better treatment and have a better chance of surviving. Computer-Aided Diagnosis (CAD) systems have emerged as valuable tools in medical imaging, assisting in the early identification of lung tumors using Computed Tomography (CT) scans. However, accurate detection remains a challenge due to the irregular shape, varying location, and low contrast of tumors in CT images. To know these challenges the study presents a novel tumor and nodule segmentation model based on a Convolutional Neural Network (CNN). The model integrates preprocessing techniques such as image filtering for enhancement and postprocessing methods utilizing morphological operations for precise segmentation. Additionally, an active contour algorithm is employed to refine tumor boundary detection. We tested the model with sensitivity analysis on a benchmark dataset.