Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation (슈퍼픽셀 이미지 분할을 이용한 ResNet 기반 백혈구 감별 알고리즘 개발)

Kyu-Man Lee (이규만); Soon Ah Kang (강순아)

doi:10.9708/jksci.2018.23.04.147

Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(4), pp.147-153
DOI : 10.9708/jksci.2018.23.04.147
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : March 27, 2018
Accepted : April 11, 2018
Published : April 30, 2018

Kyu-Man Lee ¹, Soon Ah Kang ¹

¹호서대학교

Accredited

ABSTRACT

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

KEYWORDS

Super-pixel, Residual Network, Segmentation, Classification, White Blood Cell

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Development of ResNet-based WBC Classification Algorithm Using Super-pixel Image Segmentation

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