# Imaging Modalities-Based Classification for Lung Cancer Detection

arXiv: https://arxiv.org/abs/2509.16254 · HTML: https://mlsanity.com/paper/2509.16254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Google Searches and COVID-19 Cases in Saudi Arabia: A Correlation Study](https://mlsanity.com/paper/2011.14386/) (2020)

## Abstract (arXiv)

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.