{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:35:52Z","timestamp":1792787752944,"version":"4.5.0"},"reference-count":68,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Medical Research Council Confidence in Concept Award","award":["MC PC 17171"],"award-info":[{"award-number":["MC PC 17171"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>Objective: The only possible solution to increase the patients\u2019 fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches.<\/jats:p>","DOI":"10.1177\/15330338221124372","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T07:06:08Z","timestamp":1663916768000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":46,"title":["LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification"],"prefix":"10.1177","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2303-5663","authenticated-orcid":false,"given":"Zeyu","family":"Ren","sequence":"first","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, 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