A Systematic Review on Effectiveness and Contributions of Machine Learning and Deep Learning Methods in Lung Cancer Diagnosis and Classifications

Abstract

In the current scenario of people's health, lung cancer is the principal cause of cancer-related losses, and its death rates are steadily rising. In any event, radiologists are understaffed and under pressure to work overtime, making it difficult to appropriately assess the increasing volume of image data. Consequently, several researchers have developed automated techniques for quickly and accurately predicting the development of cancer cells using medical imaging and machine learning techniques. As advances in computer-aided systems have been made, deep learning techniques have been thoroughly investigated to aid in understanding the results of computer-aided diagnosis (CADx) and computer-aided detection (CADe) in computed tomography (CT), magnetic resonance imaging (MRI), and X-ray for the identification of lung cancer. To provide a thorough review of the deep learning methods created for lung cancer diagnosis and detection, this study is being done. This study presents an overview of deep learning (DL) and machine learning (ML) approaches for applications centered on lung cancer diagnosis and the advancements of the methods being studied. This study focuses on segmentation and classification, which are the two primary deep learning methods for lung cancer detection and screening. The benefits and drawbacks of the deep learning models that are currently in use will also be covered. DL technologies can deliver accurate and efficient computer-assisted lung tumor detection and diagnosis, as shown by the subsequent analysis of the scan data. This study ends with a description of potential future studies that might enhance the use of deep learning to the creation of computer-assisted lung cancer detection systems.