The primary objective of this research report is to explore and analyse advancements in deep learning models for the automated detection and classification of lung cancer across diverse medical imaging modalities. Drawing insights from four distinct research papers, we aim to synthesize key findings, methodologies, and outcomes to provide a comprehensive overview of the current state of the art in the field. By examining the proposed convolutional neural network (CNN), and double convolutional neural network (CDNN) models, we seek to highlight their respective contributions, strengths, and potential applications in the realm of lung cancer diagnosis. Additionally, the report aims to identify common challenges faced in automated lung cancer detection and propose potential avenues for future research and development.
