Imaging Modalities-Based Classification for Lung Cancer Detection
It provides a foundational resource for researchers and clinicians to improve lung cancer detection, but it is incremental as a review paper.
This review analyzes imaging modalities for lung cancer detection, finding that 3D CNN architectures with CT scans achieve superior performance, though challenges like high false positives and dataset variability persist.
Lung cancer continues to be the predominant cause of cancer-related mortality globally. This review analyzes various approaches, including advanced image processing methods, focusing on their efficacy in interpreting CT scans, chest radiographs, and biological markers. Notably, we identify critical gaps in the previous surveys, including the need for robust models that can generalize across diverse populations and imaging modalities. This comprehensive synthesis aims to serve as a foundational resource for researchers and clinicians, guiding future efforts toward more accurate and efficient lung cancer detection. Key findings reveal that 3D CNN architectures integrated with CT scans achieve the most superior performances, yet challenges such as high false positives, dataset variability, and computational complexity persist across modalities.