Enhancing lung cancer detection through deep learning-based image segmentation

Abstract

This literature review provides a thorough examination of important works in the field of medical image analysis, with a special emphasis on the use of machine learning and deep learning algorithms for identifying lung cancer. The chosen articles include a wide range of approaches, including three-dimensional deep learning on low-dose chest CT images and the utilization of convolutional neural networks (CNNs) for accurately detecting pulmonary nodules. Every study has specific goals, such as improving the precision of lung cancer categorization, minimizing incorrect positive results in nodule identification, and streamlining diagnostic procedures through automation. In addition to identifying lung cancer, the paper explores the wider field of medical image analysis, including the accurate categorization of skin cancer at the level of dermatologists and the segmentation of brain tumors using MRI scans. The combined discoveries not only enhance the continuous development of algorithmic methods in the medical field but also shed light on innovative architectural designs, the capacity to transmit features in deep neural networks, and the interpretability of model conclusions. This compilation of influential research highlights the ongoing advancement and broadening of machine learning and deep learning applications in healthcare diagnostics. It offers valuable insights for researchers, practitioners, and stakeholders interested in the convergence of technology and medical imaging. The combination of this research provides a detailed and complex understanding of the present condition and future directions of this evolving subject. It highlights the diverse ways in which modern computational approaches contribute to enhancing medical diagnoses and patient outcomes.