High accuracy detection for T-cells and B-cells using deep convolutional neural networks

ROBOMECH Journal

Koji Horio¹  Bilal Turan¹  Fumihito Arai¹  Taisuke Masuda¹  Yasuyuki Miyata²  Toshiki I. Saito²  Anas Mohd Noor³ 

[1] Department of Micro-Nano Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan;National Hospital Organization Nagoya Medical Center, Nagoya, Japan;School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia

关键词: Deep learning;    Convolutional neural networks;    Microfluidic chip;    T-cells;    B-cells;   

DOI  :  10.1186/s40648-018-0128-4

学科分类：人工智能

来源: Springer

【 摘 要 】

Providing an accurate count of total leukocytes and specific subsets (such as T-cells and B-cells) within small amounts of whole blood is a rather challenging ordeal due to the lack of techniques that enable the separation of leukocytes from a limited amount of whole blood. In a previous study we designed a microfluidic chip utilizing a micropillar array to isolate T-cells and B-cells from the sub-microliter of whole blood. Due to the variability of cells in size, morphology and color intensity, a Histogram of Oriented Gradients (HOG) based Support Vector Machine (SVM) classifier was proposed with an average accuracy of 94%, specificity of 99% and sensitivity of 90%. The HOG can separate the cells from the background with a high accuracy rate however, some noise is similar in shape and size to the actual cells and this results in misclassification. To alleviate this situation, in this study a convolutional neural network is trained and used to distinguish T-cells and B-cells with an accuracy rate of 98%, a specificity of 99% and a sensitivity of 97%. We also propose an HOG feature based SVM classifier to preselect the detection windows to accelerate the detection to process images in less than 10 min. The proposed on-chip cell detecting and counting method will be useful for numerous applications in diagnosis and for monitoring diseases.

【 授权许可】

CC BY