Lung Cancer Detection Using Machine Learning

Abstract

Uncontrolled cell growth in lung tissues is a possible cause of lung cancer. One of the primary causes of cancer-related deaths worldwide is lung cancer. Recovery from lung cancer requires an early diagnosis. The major cause of cancer-related deaths this century has been lung cancer, and this trend is expected to continue in the decades to come. Lung cancer is treatable if the disease's symptoms are found at an early stage. Many reliable systems for the treatment of lung cancer that are simple to use and lower in cost have been developed using the most popular data science technology. This research presents a comparative study of several machine learning-based methods from the last three years for the identification of lung cancer. There are an excessive number of methods now available to detect lung cancer, most of which depend on CT scans and others on x-ray images. To detect lung cancer, almost all of them are using image classification methods to find lung cancer nodules. This is combined with several segmentation techniques and a wide range of classifier algorithms to get a more accurate result.