Gupta, B B, Almomani, A, Chui, Kwok Tai, Jhaveri, Rutvij H., Chi, Hao Ran, Arya, Varsha, Nauman, Ali and Qi, Lianyong (2023) Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection. International Journal of Intelligent Systems, 2023. pp. 1-14. ISSN 0884-8173
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Abstract
Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small-scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL-MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL-MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL-MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.
Affiliation: | Skyline University College |
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SUC Author(s): | Gupta, B B and Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 |
All Author(s): | Gupta, B B, Almomani, A, Chui, Kwok Tai, Jhaveri, Rutvij H., Chi, Hao Ran, Arya, Varsha, Nauman, Ali and Qi, Lianyong |
Item Type: | Article |
Subjects: | B Information Technology > BL Machine Learning B Information Technology > BM Artificial Intelligence B Information Technology > BW Computer Networks |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 18 Dec 2023 16:14 |
Last Modified: | 18 Dec 2023 16:14 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/669 |
Publisher URL: | https://doi.org/10.1155/2023%2F6376275 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/14768 |
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