Machine Learning Approaches for Detecting Lung Cancer: A Review

Abstract

One of the illnesses that can be lethal and caused by cell division is cancer. For the previous few years, it has been the second most frequent cause of death worldwide. Numerous conditions together referred to as any part of the body might be affected by cancer. Malignant tumors and neoplasms are other terms used to characterize cancer. One of the body parts that may be affected is the lungs. The superior cause of mortality related to cancer globally is lung cancer. It is essential to detect lung cancer early in order to lower the death rate of those who have the disease. This paper offers a review of recently published research in diagnosing lung cancer, emphasizing the application of Machine Learning (ML) and Deep Learning (DL) techniques in identifying and classifying cancer from diverse medical data sources such as X-rays and CT scans. This paper also provides a comparison of the commonly used datasets in terms of the number of classes, screening type, commonly used preprocessing and if segmentation is usually needed.