TY - GEN
T1 - White Blood Cells Classification
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
AU - Mahendren, Aniel
AU - P.P. Abdul Majeed, Anwar
AU - Ab Nasir, Ahmad Fakhri
AU - Luo, Yang
AU - Aslam, Saad
AU - Behjati, Mehran
AU - Jasser, Muhammed Basheer
AU - Mohd Khairuddin, Ismail
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - White Blood Cells (WBCs) are vital components of the immune system, playing a crucial role in defending the body against infections and diseases. Traditionally, the classification of WBCs was performed manually by hematologists, a method fraught with limitations such as subjectivity and time consumption. To address these challenges, this study explores the efficacy of a transfer learning pipeline for WBC classification, leveraging pre-trained models like VGG16, VGG19, and InceptionV3 for feature extraction. The classification models employed include k-Nearest Neighbour (kNN), Support Vector Machine (SVM), and Logistic Regression (LR). The research utilizes Paul Mooney’s white blood cell dataset, which consists of 640 microscopic images of four different WBC types. The dataset is divided into training, validation, and testing sets in a 70:15:15 ratio. The results demonstrate that the VGG16-LR pipeline achieves superior classification accuracy compared to other model combinations tested. This study highlights the potential of integrating transfer learning with robust classifiers to improve the accuracy and efficiency of WBC classification, offering promising implications for medical diagnostics and treatment planning.
AB - White Blood Cells (WBCs) are vital components of the immune system, playing a crucial role in defending the body against infections and diseases. Traditionally, the classification of WBCs was performed manually by hematologists, a method fraught with limitations such as subjectivity and time consumption. To address these challenges, this study explores the efficacy of a transfer learning pipeline for WBC classification, leveraging pre-trained models like VGG16, VGG19, and InceptionV3 for feature extraction. The classification models employed include k-Nearest Neighbour (kNN), Support Vector Machine (SVM), and Logistic Regression (LR). The research utilizes Paul Mooney’s white blood cell dataset, which consists of 640 microscopic images of four different WBC types. The dataset is divided into training, validation, and testing sets in a 70:15:15 ratio. The results demonstrate that the VGG16-LR pipeline achieves superior classification accuracy compared to other model combinations tested. This study highlights the potential of integrating transfer learning with robust classifiers to improve the accuracy and efficiency of WBC classification, offering promising implications for medical diagnostics and treatment planning.
KW - Deep Learning
KW - Feature Extractors
KW - Machine Learning
KW - Transfer Learning
KW - White Blood Cells
UR - http://www.scopus.com/inward/record.url?scp=105002711736&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_63
DO - 10.1007/978-981-96-3949-6_63
M3 - Conference Proceeding
AN - SCOPUS:105002711736
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 757
EP - 763
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 -