DOI QR코드

DOI QR Code

전이 학습을 이용한 VGG19 기반 말라리아셀 이미지 인식

Malaria Cell Image Recognition Based On VGG19 Using Transfer Learning

  • ;
  • 김강철 (전남대학교 공학대학 전기컴퓨터공학부)
  • 투고 : 2022.05.17
  • 심사 : 2022.06.17
  • 발행 : 2022.06.30

초록

말라리아는 기생충에 의해 발생하는 질병으로 전 세계에 퍼져있다. 말라리아 셀을 인식하는데 일반적으로 두꺼운 혈흔과 얇은 혈흔 검사 방법이 사용되지만 이러한 방법은 많은 수작업 계산이 필요하여 효율성과 정확성이 매우 낮을 뿐만 아니라 빈민국에는 병리학자가 부족하여 말라리아 치명율이 높다. 본 논문에서는 특징 추출기, 잔류 구조와 완전 연결층으로 구성되고, 전이 학습을 이용한 말라리아셀 이미지를 인식하는 모델을 제안한다. VGG-19 모델의 사전 학습된 파라미터가 사용될 때 일부 컨볼루션층의 파라미터는 고정되고, 모델의 데이터에 맞추기 위하여 미세조정이 사용된다. 그리고 제안된 모델과 비교하기 위하여 잔류 구조가 없는 말라리아셀 인식 모델을 구현한다. 실험 결과 잔류 구조를 사용한 모델이 잔류 구조가 없는 모델에 비하여 성능이 우수 하였으며, 최신 논문과 비교하여 가장 높은 97.33%의 정확도를 보여주었다.

Malaria is a disease caused by a parasite and it is prevalent in all over the world. The usual method used to recognize malaria cells is a thick and thin blood smears examination methods, but this method requires a lot of manual calculation, so the efficiency and accuracy are very low as well as the lack of pathologists in impoverished country has led to high malaria mortality rates. In this paper, a malaria cell image recognition model using transfer learning is proposed, which consists in the feature extractor, the residual structure and the fully connected layers. When the pre-training parameters of the VGG-19 model are imported to the proposed model, the parameters of some convolutional layers model are frozen and the fine-tuning method is used to fit the data for the model. Also we implement another malaria cell recognition model without residual structure to compare with the proposed model. The simulation results shows that the model using the residual structure gets better performance than the other model without residual structure and the proposed model has the best accuracy of 97.33% compared to other recent papers.

키워드

참고문헌

  1. WHO, "World malaria report 2021," Report, 2021.
  2. WHO, "Malaria: fact sheet," Report, 2014.
  3. J. Duncan and L. Hartley, ''Blood Smear Examination: Normal Cells,'' Veterinary Nursing Journal, vol. 15, no. 6, 2014, pp. 231-234.
  4. N. J. Shah, Introduction to Basics of Pharmacology and Toxicology. Singapore: Springer 2019.
  5. T. Visser, J. Daily, N. Hotte, C. Dolkart, J. Cunningham, and P. Yadav, ''Rapid diagnostic tests for malaria,'' Bulletin of the World Health Organization, vol. 93, no. 12, 2015, pp. 862-866. https://doi.org/10.2471/BLT.14.151167
  6. WHO, ''Malaria microscopy quality assurance manual-Ver.2,'' Report, 2016.
  7. W. Shang, K. Sohn, D. Almeida, and H. Lee, "Understanding and improving convolutional neural networks via concatenated rectified linear units," 33rd International Conference on Machine Learning, New York, USA, 2016.
  8. P. Moeskops, J. Wolterink, B. van der Velden, K. Gilhuijs, T. Leiner, M. Viergever, and I. Isgum, ''Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities,'' International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 2016.
  9. K. Maninis, J. Pont-Tuset, P. Arbelaez, and L. Van Gool, ''Deep Retinal Image Understanding,'' International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 2016.
  10. Y. Sato and G. Gerig, ''MICCAI: Medical Image Computing and Computer-Assisted Intervention,'' Academic Radiology, vol. 10, no. 12, 2003, pp. 1339-1340. https://doi.org/10.1016/S1076-6332(03)00614-7
  11. J. H. Bong, S. H. Jeong, S. Jeong, and J. Han, ''Study on Image Use for Plant Disease Classification,'' Journal of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 2, 2022, pp. 343-350. https://doi.org/10.13067/JKIECS.2022.17.2.343
  12. M. Kim, ''A Study on the Sports Rehabilitation Treatment for the lntellectual Disabilities using deep learning,'' Journal of the Korea Institute of Electronic Communication Sciences, vol. 15, no. 4, 2020, pp. 725-732. https://doi.org/10.13067/JKIECS.2020.15.4.725
  13. L. Rosado, J. M. Correia da Costa, D. Elias, and J. S. Cardoso, "A Review of Automatic Malaria Parasites Detection and Segmentation in Microscopic Images," Anti-Infective Agents, vol. 14, no. 1, 2016, pp. 11-22.
  14. F. B. Tek, A. G. Dempster, and I. Kale, "Parasite detection and identification for automated thin blood film malaria diagnosis," Computer vision and image understanding, vol. 114, no. 1, 2010, pp. 21-32. https://doi.org/10.1016/j.cviu.2009.08.003
  15. D. K. Das, R. Mukherjee, and C. Chakraborty, "Computational microscopic imaging for malaria parasite detection: a systematic review," Journal of microscopy, vol. 260, no. 1, 2015, pp. 1-19. https://doi.org/10.1111/jmi.12270
  16. N. E. Ross, C. J. Pritchard, D. M. Rubin, and A. G. Duse, "Automated image processing method for the diagnosis and classification of malaria on thin blood smears," Medical and Biological Engineering and Computing, vol. 44, no. 5, 2006, pp. 427-436. https://doi.org/10.1007/s11517-006-0044-2
  17. S. Sinha, U. Srivastava, V. Dhiman, P. S. Akhilan, and S. Mishra, ''Performance assessment of deep learning procedures: Sequential and ResNet on malaria dataset,'' Journal of Robotics and Control (JRC), vol. 2, no. 1, 2021, pp. 12-18.
  18. S. Tasdemir and M. M. Qanbar, ''Detection of Malaria Diseases with Residual Attention Network,'' International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 4, 2019, pp. 238-244. https://doi.org/10.18201/ijisae.2019457677
  19. A. S. B. Reddy and D. S. Juliet, ''Transfer Learning with ResNet-50 for Malaria Cell-Image Classification,'' International Conference on Communication and Signal Processing, Chennai, India, 2019.
  20. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, ''Recent advances in convolutional neural networks,'' Pattern Recognition, vol. 77, 2018, pp. 354-377. https://doi.org/10.1016/j.patcog.2017.10.013
  21. Z. Liang, A. Powell, I. Ersoy, M. Poostchi, K. Silamut, K. Palaniappan, P. Guo, M. Hossain, A. Sameer, R. Maude, J. Huang, S. Jaeger, and G. Thoma, ''CNN based analysis for malaria diagnosis,'' International conference on bioinformatics and biomedicine(BIBM), Shenzhen China, 2016.
  22. R. Harini and N. Sheela, "Feature Extraction and Classification of Retinal Images for Automated Detection of Diabetic Retinopathy," Second International Conference on Cognitive Computing and Information Processing, Mysuru India, 2016.
  23. K. Simonyan and A. Zisserman, ''Very Deep Convolutional Networks for Large-Scale Image Recognition Karen,'' American Journal of Health-System Pharmacy, vol. 75, no. 6, 2018, pp. 398-406. https://doi.org/10.2146/ajhp170251
  24. O. Wichrowska, N. Maheswaranathan, M. Hoffman, S. Colmenarejo, M. Denii, N. De Freitas, and S.-D. Jascha, ''Learned optimizers that scale and generalize,'' 34th International Conference on Machine Learning, Sydney, Australia, 2017.