{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:53:21Z","timestamp":1788188001611,"version":"build-2803163510"},"reference-count":29,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University, Saudi Arabia","award":["TU-DSPP-2024-52"],"award-info":[{"award-number":["TU-DSPP-2024-52"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34\u2019s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.7717\/peerj-cs.2903","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T08:40:32Z","timestamp":1748335232000},"page":"e2903","source":"Crossref","is-referenced-by-count":7,"title":["Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2"],"prefix":"10.7717","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0822-1532","authenticated-orcid":true,"given":"Pranshu","family":"Saxena","sequence":"first","affiliation":[{"name":"School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay 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