Convolutional Neural Network Approach for White Blood Cells Classification

Convolutional Neural Network Approach for White Blood Cells Classification

Nandhini R
Department of Computer Science and Engineering

Sethu Institute of Technology
Virudhunagar, India.
nandhinirm92@gmail.com

Pabitha muthu P
Department of Computer Science and Engineering

Sethu Institute of Technology
Virudhunagar, India.
pabithapraveenit@sethu.ac.in

Abstract— White blood cells (WBCs) or leukocytes are immune cells responsible for protecting the body against infections, engulfing foreign invaders, repairing tissue injuries, and resisting pathogens or diseases. WBCs such as macrophages, neutrophils, T cells, and natural killer cells (NK cells) are essential players in many important diseases. For example, in cancer, WBCs remodel the tumor extracellular matrix, facilitate tumor cell migration and invasion, and modulate tumor angiogenesis. Through these activities, WBCs promote all stages of tumor progression. The system is developed the DL algorithms such as convolutional neural network for predicting the types of blood cell disease. Finally, the system can estimate some performance metrics such as accuracy and error rate.

Keywords—Image Segmentation, White blood cells, Convolutional Neural Network, Deep learning, Blood leukocytes segmentation.