Biomedical Image Analysis for Lung Cancer Detection Using Deep Learning

Abstract

Lung cancer is a significant global health concern and early detection plays a crucial role in improving patient outcomes. With the advancements in medical imaging technologies, such as computed tomography (CT) and positron emission tomography (PET), biomedical images have become an invaluable tool for diagnosing and monitoring lung cancer. Deep learning, a subfield of machine learning, has emerged as a powerful technique for automated analysis of biomedical images. This chapter presents a comprehensive review of the current state-of-the-art in deep learning-based approaches for lung cancer detection using biomedical images. The study encompasses a wide range of techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, such as 3D CNNs and attention mechanisms. The review focuses on the various stages involved in lung cancer detection, including image pre-processing, feature extraction, and classification. It discusses the challenges associated with these stages and highlights the solutions proposed by different studies.