Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature
The work provides a literature mapping of AI and DL methods—especially CNNs with data augmentation and transfer learning—applied to lung cancer image analysis, outlining trends and reported performance.
Key limitations identified include lack of standardized datasets, explainability of models, patient privacy, and ethical implications, which must be addressed for responsible clinical use.
Why it matters: Highlights potential for earlier lung cancer diagnosis through AI-assisted imaging, while stressing necessary safeguards and regulatory considerations to ensure safe implementation.
Primary source: cs.LG updates on arXiv.orgOpen source ↗
Entities:96 articlesArtificial IntelligenceConvolutional Neural Networksdata augmentationlung cancerradiologytransfer learning
AI-assisted brief
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