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.
