Computer-Assisted Lung Cancer Diagnosis through Morphological Analysis & CNN

Lung cancer is an unregulated development of cells that begins in the lung and spreads to other parts of the body, posing a significant risk to human life. Radiological imaging, such as computed tomography (CT) scans and X-rays, is the primary tool for diagnosing lung cancer. However, a person's ability to interpret a large number of CT images might vary greatly, especially when the scans show many gray level fluctuations. The purpose of this study is to use Python-based machine learning and image processing approaches to detect lung cancer. Using the National Center for Cancer Diseases lung cancer dataset, this paper analyzes lung scans to determine if they are malignant or non-cancerous. Based on the study's top-performing solution, the code first preprocesses the images before applying segmentation and feature extraction techniques. The suggested approach makes a cancer prediction based on retrieved properties that were obtained through morphological processing.