Review on Use of Machine Learning Approach for Lung Cancer Detection

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection of lung cancer plays a crucial role in improving patient outcomes and survival rates. In recent years, machine learning techniques have shown great potential in aiding the early detection and diagnosis of lung cancer. This paper presents a comprehensive review of the application of machine learning algorithms and methodologies for lung cancer detection. We discuss various data sources, including medical imaging data such as computed tomography. (CT) scans and histopathology images, as well as clinical data and genomic data. We review different machine learning approaches, including supervised, unsupervised, and deep learning methods, highlighting their strengths and limitations. Furthermore, we discuss feature extraction and selection techniques, as well as model evaluation and performance metrics employed in lung cancer detection studies Finally, we identify current challenges and future directions in the field, emphasizing the importance of robust and interpretable machine learning models for accurate lung cancer detection

Keywords: lung cancer, machine learning, early detection, medical imaging, deep learning, feature extraction, model evaluation, performance metrics