Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines

Darren Soong Kai Xuan; Anwar P.P. Abdul Majeed; Rabiu Muazu Musa; Yang Luo; Saad Aslam; Samuel Soma M. Ajibade; Mehran Behjati; Muhammed Basheer Jasser; Muhammad Amirul Abdullah

doi:10.1007/978-981-96-3949-6_62

Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines

Darren Soong Kai Xuan
, Anwar P.P. Abdul Majeed^*
, Rabiu Muazu Musa
, Yang Luo
, Saad Aslam
, Samuel Soma M. Ajibade
, Mehran Behjati
, Muhammed Basheer Jasser
, Muhammad Amirul Abdullah

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Lung cancer remains a significant global health challenge, with early detection being crucial for improving patient outcomes. This study explores the use of transfer learning in enhancing the diagnostic accuracy of lung cancer classification from CT images. Specifically, it compares the effectiveness of two transfer learning pipelines utilizing pre-trained models, VGG19 and ResNet152, as feature extractors combined with Logistic Regression (LR) for classification. The results demonstrate that the VGG16 + LR pipeline outperforms the ResNet152 pipeline, achieving superior classification accuracy for both validation and testing datasets at 97%, respectively. This finding underscores the potential of transfer learning in medical imaging, offering a promising approach to improving early detection and treatment strategies for lung cancer.

Original language	English
Title of host publication	Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
Editors	Wei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	750-756
Number of pages	7
ISBN (Print)	9789819639489
DOIs	https://doi.org/10.1007/978-981-96-3949-6_62
Publication status	Published - 2025
Event	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China Duration: 22 Aug 2024 → 23 Aug 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1316 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/Territory	China
City	Suzhou
Period	22/08/24 → 23/08/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Deep Learning
Feature Extractors
Lung Cancer
Machine Learning
Transfer Learning

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10.1007/978-981-96-3949-6_62

Cite this

Xuan, D. S. K., P.P. Abdul Majeed, A., Musa, R. M., Luo, Y., Aslam, S., Ajibade, S. S. M., Behjati, M., Jasser, M. B., & Abdullah, M. A. (2025). Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines. In W. Chen, A. H. Ping Tan, Y. Luo, L. Huang, Y. Zhu, A. PP Abdul Majeed, F. Zhang, Y. Yan, & C. Liu (Eds.), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics (pp. 750-756). (Lecture Notes in Networks and Systems; Vol. 1316 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-3949-6_62

Xuan, Darren Soong Kai ; P.P. Abdul Majeed, Anwar ; Musa, Rabiu Muazu et al. / Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification : Insights from VGG19 and ResNet152 Pipelines. Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. editor / Wei Chen ; Andrew Huey Ping Tan ; Yang Luo ; Long Huang ; Yuyi Zhu ; Anwar PP Abdul Majeed ; Fan Zhang ; Yuyao Yan ; Chenguang Liu. Springer Science and Business Media Deutschland GmbH, 2025. pp. 750-756 (Lecture Notes in Networks and Systems).

@inproceedings{e4dc186b32b6418da253e11bc2d701bd,

title = "Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines",

abstract = "Lung cancer remains a significant global health challenge, with early detection being crucial for improving patient outcomes. This study explores the use of transfer learning in enhancing the diagnostic accuracy of lung cancer classification from CT images. Specifically, it compares the effectiveness of two transfer learning pipelines utilizing pre-trained models, VGG19 and ResNet152, as feature extractors combined with Logistic Regression (LR) for classification. The results demonstrate that the VGG16 + LR pipeline outperforms the ResNet152 pipeline, achieving superior classification accuracy for both validation and testing datasets at 97\%, respectively. This finding underscores the potential of transfer learning in medical imaging, offering a promising approach to improving early detection and treatment strategies for lung cancer.",

keywords = "Deep Learning, Feature Extractors, Lung Cancer, Machine Learning, Transfer Learning",

author = "Xuan, \{Darren Soong Kai\} and \{P.P. Abdul Majeed\}, Anwar and Musa, \{Rabiu Muazu\} and Yang Luo and Saad Aslam and Ajibade, \{Samuel Soma M.\} and Mehran Behjati and Jasser, \{Muhammed Basheer\} and Abdullah, \{Muhammad Amirul\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 ; Conference date: 22-08-2024 Through 23-08-2024",

year = "2025",

doi = "10.1007/978-981-96-3949-6\_62",

language = "English",

isbn = "9789819639489",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "750--756",

editor = "Wei Chen and \{Ping Tan\}, \{Andrew Huey\} and Yang Luo and Long Huang and Yuyi Zhu and \{PP Abdul Majeed\}, Anwar and Fan Zhang and Yuyao Yan and Chenguang Liu",

booktitle = "Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics",

}

Xuan, DSK, P.P. Abdul Majeed, A, Musa, RM, Luo, Y, Aslam, S, Ajibade, SSM, Behjati, M, Jasser, MB & Abdullah, MA 2025, Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines. in W Chen, AH Ping Tan, Y Luo, L Huang, Y Zhu, A PP Abdul Majeed, F Zhang, Y Yan & C Liu (eds), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. Lecture Notes in Networks and Systems, vol. 1316 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 750-756, 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024, Suzhou, China, 22/08/24. https://doi.org/10.1007/978-981-96-3949-6_62

Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines. / Xuan, Darren Soong Kai; P.P. Abdul Majeed, Anwar; Musa, Rabiu Muazu et al.
Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. ed. / Wei Chen; Andrew Huey Ping Tan; Yang Luo; Long Huang; Yuyi Zhu; Anwar PP Abdul Majeed; Fan Zhang; Yuyao Yan; Chenguang Liu. Springer Science and Business Media Deutschland GmbH, 2025. p. 750-756 (Lecture Notes in Networks and Systems; Vol. 1316 LNNS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification

T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

AU - Xuan, Darren Soong Kai

AU - P.P. Abdul Majeed, Anwar

AU - Musa, Rabiu Muazu

AU - Luo, Yang

AU - Aslam, Saad

AU - Ajibade, Samuel Soma M.

AU - Behjati, Mehran

AU - Jasser, Muhammed Basheer

AU - Abdullah, Muhammad Amirul

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Lung cancer remains a significant global health challenge, with early detection being crucial for improving patient outcomes. This study explores the use of transfer learning in enhancing the diagnostic accuracy of lung cancer classification from CT images. Specifically, it compares the effectiveness of two transfer learning pipelines utilizing pre-trained models, VGG19 and ResNet152, as feature extractors combined with Logistic Regression (LR) for classification. The results demonstrate that the VGG16 + LR pipeline outperforms the ResNet152 pipeline, achieving superior classification accuracy for both validation and testing datasets at 97%, respectively. This finding underscores the potential of transfer learning in medical imaging, offering a promising approach to improving early detection and treatment strategies for lung cancer.

AB - Lung cancer remains a significant global health challenge, with early detection being crucial for improving patient outcomes. This study explores the use of transfer learning in enhancing the diagnostic accuracy of lung cancer classification from CT images. Specifically, it compares the effectiveness of two transfer learning pipelines utilizing pre-trained models, VGG19 and ResNet152, as feature extractors combined with Logistic Regression (LR) for classification. The results demonstrate that the VGG16 + LR pipeline outperforms the ResNet152 pipeline, achieving superior classification accuracy for both validation and testing datasets at 97%, respectively. This finding underscores the potential of transfer learning in medical imaging, offering a promising approach to improving early detection and treatment strategies for lung cancer.

KW - Deep Learning

KW - Feature Extractors

KW - Lung Cancer

KW - Machine Learning

KW - Transfer Learning

UR - https://www.scopus.com/pages/publications/105002722027

U2 - 10.1007/978-981-96-3949-6_62

DO - 10.1007/978-981-96-3949-6_62

M3 - Conference Proceeding

AN - SCOPUS:105002722027

SN - 9789819639489

T3 - Lecture Notes in Networks and Systems

SP - 750

EP - 756

BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics

A2 - Chen, Wei

A2 - Ping Tan, Andrew Huey

A2 - Luo, Yang

A2 - Huang, Long

A2 - Zhu, Yuyi

A2 - PP Abdul Majeed, Anwar

A2 - Zhang, Fan

A2 - Yan, Yuyao

A2 - Liu, Chenguang

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 August 2024 through 23 August 2024

ER -

Xuan DSK, P.P. Abdul Majeed A, Musa RM, Luo Y, Aslam S, Ajibade SSM et al. Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines. In Chen W, Ping Tan AH, Luo Y, Huang L, Zhu Y, PP Abdul Majeed A, Zhang F, Yan Y, Liu C, editors, Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. Springer Science and Business Media Deutschland GmbH. 2025. p. 750-756. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-3949-6_62

Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines

Abstract

Publication series

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UN SDGs

Keywords

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