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Date Published: 28/11/2025
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DOI: 10.54607/hcmue.js.22.11.5018(2025)
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Vol. 22 No. 11 (2025)
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Articles
How to Cite
Trinh, H. H., Tran, V. T., Nguyen, L. M. N., Nguyen, D. Q. B., & Le, H. T. V. (2025). USE OF DEEP LEARNING MODELS FOR LUNG CANCER DIAGNOSIS. Ho Chi Minh City University of Education Journal of Science, 22(11), 1993-2005. https://doi.org/10.54607/hcmue.js.22.11.5018(2025)

USE OF DEEP LEARNING MODELS FOR LUNG CANCER DIAGNOSIS

Trinh Huy Hoang1, , Tran Van Tuan1, Nguyen Le Minh Ngoc1, Nguyen Duong Quoc Bao1, Le Hong Thuy Vu2
1 Ho Chi Minh City University of Education, Vietnam
2 Faculty of Information Technology, Ho Chi Minh City University of Foreign Languages and Information Technology, Vietnam

Main Article Content

Abstract

Advances in modern medicine have facilitated the early detection of lung cancer. This study proposes a novel deep learning approach by modifying the traditional U-Net architecture, in which the decoder part is entirely removed and replaced with a specialized classification branch appended to the encoder. This change helps the model focus on extracting global image features relevant to classification, rather than reconstructing pixel-level segmentation maps. This not only significantly reduces the computational complexity but also maintains the ability to learn deep features from medical images for lung cancer detection and diagnosis. Based on the IQ-OTH/NCCD dataset, the improved U-Net model outperforms other deep learning methods, including VGG-16, ResNet-50, NasNet Mobile, and ViT.

Keywords

deep learning method, lung cancer diagnosis, U-Net model

Article Details

References

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