슈퍼픽셀 이미지 분할을 이용한 ResNet 기반 백혈구 감별 알고리즘 개발

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 Y. M. Alomari, 1-17, 2014

2 현정환, "객체 검출 시스템 개발을 위한 Tracking-Learning-Detection 알고리즘과 학습알고리즘에 관한 연구" 한국정보기술학회 15 (15): 139-145, 2017

3 Shlee, "White blood cell image Retrieving & Clustering System" 26 (26): 530-532, 1999

4 M. D. Zeiler, "Visualizing and understanding convolutional networks" 818-833, 2014

5 K. Simonyan, "Very deep convolutional networks for large-scale image recognition, Vol. 6" 1-14, 2015

6 N. Ghane, "Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm" 7 (7): 92-101, 2017

7 R. Achanta, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods" Institute of Electrical and Electronics Engineers (IEEE) 34 (34): 2274-2282, 2012

8 "Pattern Recognition, Pattern Analysis"

9 M. I. Razzak, "Microscopic Blood Smear Segmentation and Classification using Deep Contour Aware CNN and Extreme Machine Learning" 49-55, 2017

10 A. Krizhevsky, "Imagenet classification with deep convolutional neural networks" 1097-1105, 2012

11 A. Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks" 1 : 1097-1105, 2012

12 최종문, "Image Analysis Software를 이용한 변형적혈구 측정법 개발" 대한진단검사의학회 3 (3): 6-14, 2013

13 "Identity Mappings in Deep Residual Networks Review"

14 K. He, "Identity Mappings in Deep Residual Networks" 3 : 1-15, 2016

15 M. Liu, "Entropy rate superpixel segmentation" 1-14, 2014

16 K. He, "Deep residual learning for image recognition" 770-778, 2015

17 Jsyoo, "Deep learning based image recognition technology trend" Information and Society 17-24, 2017

18 Y. Tang, "Deep Learning using Linear Support Vector Machines" 4 : 2015

19 I. Goodfellow, "Deep Learning" MIT Press 2016

20 M. Habibzadeh, "Comparati ve study of shape, intensity and texture features and support vector machine for white blood cell classification" 7 (7): 20-35, 2013

21 S. Ioffe, "Batch Normalization: Acceler ating Deep Network Training by Reducing Internal Covariate Shift" 3 : 1-11, 2015

22 R. Sorgedrager, "Automated malaria diagnosis using convolutional neural networks in an on-field setting" Delft University of Technology 2018

23 M. Xu, "A deep convolutional neural network for classification of red blood cells in sickle cell anemia" 13 (13): 1-27, 2017

24 H. N. Mhaskar, "A Deep Learning Approach to Diabetic Blood Glucose Prediction" 3 (3): 1-11, 2017