Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2
The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings.
Authors
- Mamoon Rashid (ORCID: https://orcid.org/0000-0003-4851-4994)
- Sultan S. Alshamrani (ORCID: https://orcid.org/0000-0001-8194-9354)
- Mrim M. Alnfiai (ORCID: https://orcid.org/0000-0003-3837-6313)
- Sanjay Kumar Singh (ORCID: https://orcid.org/0000-0002-7101-4341)
- Pranshu Saxena (ORCID: https://orcid.org/0000-0002-0822-1532)
Institutions
- Guru Gobind Singh Indraprastha University (IN)
- Taif University (SA)
- Bennett University (IN)
- Bahrain Polytechnic (BH)
Publication Details
- Journal
- PeerJ Computer Science
- Published
- 2025-05-27
- DOI
- https://doi.org/10.7717/peerj-cs.2903
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96