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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.

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White Blood Cells Classification: A Feature-Based Transfer Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Aniel Mahendren,
  • Anwar P.P. Abdul Majeed,
  • Ahmad Fakhri Ab Nasir,
  • Yang Luo,
  • Saad Aslam,
  • Mehran Behjati,
  • Muhammed Basheer Jasser,
  • Ismail Mohd Khairuddin

摘要

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.