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
Updated 34 min ago · first seen 11 Sept 2026
paper_01M294FNQXQNZW00148Z1N5BG0
- Published
- 11 Sept 2026
- T1 · 35 min ago
- arXiv
- 2609.10652
- T1 · 35 min ago
- Category
- cs.LG
- T1 · 35 min ago
Abstract
Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and requires trained experts. In this context, artificial intelligence (AI) and deep learning (DL) emerge as potential tools to automate and optimize image analysis. The objective of this work is to review the most recent and relevant applications of AI and DL in the field of radiology for the detection of lung cancer. To this end, an exhaustive search was carried out in scientific databases such as PubMed,IEEEXPLORE, Scopus and Web of Science, and 96 articles published from 2015 to the present addressing the use of AI and DL in biomedical engineering were selected. Emphasis is placed on the use of convolutional neural networks (CNN) with transfer learning and Data Augmentation as promising techniques to improve the accuracy and efficiency of the image interpretation process. The results show that the use of AI and DL can offer an effective alternative for the early diagnosis of lung cancer, with high sensitivity and specificity. However, current limitations and challenges that must be addressed to guarantee its responsible and safe application in clinical practice are also identified, such as the lack of standardized data, the ex plainability of the models, patient privacy, and the ethical and social implications. It is concluded that the use of AI and DL can have a positive impact on the care of patients with lung cancer, but further research and regulation are required to ensure its quality and reliability.
Authors 1
Pablo Ramirez Amador
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Arxiv announce type
- cross
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- arXiv id
- 2609.10652
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Categories
- cs.LG, cs.CL
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
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Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
35 min ago
Conflicts
None
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- Authors
- Pablo Ramirez Amador
As of
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and requires trained experts. In this context, artificial intelligence (AI) and deep learning (DL) emerge as potential tools to automate and optimize image analysis. The objective of this work is to review the most recent and relevant applications of AI and DL in the field of radiology for the detection of lung cancer. To this end, an exhaustive search was carried out in scientific databases such as PubMed,IEEEXPLORE, Scopus and Web of Science, and 96 articles published from 2015 to the present addressing the use of AI and DL in biomedical engineering were selected. Emphasis is placed on the use of convolutional neural networks (CNN) with transfer learning and Data Augmentation as promising techniques to improve the accuracy and efficiency of the image interpretation process. The results show that the use of AI and DL can offer an effective alternative for the early diagnosis of lung cancer, with high sensitivity and specificity. However, current limitations and challenges that must be addressed to guarantee its responsible and safe application in clinical practice are also identified, such as the lack of standardized data, the ex plainability of the models, patient privacy, and the ethical and social implications. It is concluded that the use of AI and DL can have a positive impact on the care of patients with lung cancer, but further research and regulation are required to ensure its quality and reliability. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
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: arxiv announce type changed from new to cross
Arxiv announce typenew→crossarxivNew paper: 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
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CL | feed | T1· Official | 34 min ago | 1 |
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 35 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.