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 - http://www.scopus.com/inward/record.url?scp=105002722027&partnerID=8YFLogxK
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 -
