Analysis of Medical Images Classification Methods: the Case of Neutrophil Nuclei
Paper #8811 received 5 Mar 2023; revised manuscript received 27 May 2023; accepted for publication 10 Jun 2023; published online 9 Jul 2023.
1. J. Actor, Elsevier’s Integrated Review Immunology and Microbiology, 2nd ed., Elsevier (2012). ISBN: 9780323074476.
2. D. Zucker-Franklin, M. F. Greaves, C. E. Grossi, and A. M. Marmont, “Neutrophils, Atlas of Blood Cells: Function and Pathology,” Lea and Ferbiger, Philadelphia 1(2), (1988).
3. T. Go, H. Byeon, and S. J. Lee, “Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning,” Biosensors and Bioelectronics 103, 12–18 (2018).
4. Y. G. Kim, Y. Jo, Y. Cho, H. S. Min, and Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosensors and Bioelectronics 123, 69–76 (2019).
5. V. K. Ilyin, Z. O. Solovieva, M. A. Skedina, N. V. Verdenskaya, K. V. Volkova, and I. A. Ivanova, “Choice of an optimal set of signs and evaluation of the quality of microbial objects recognition by their images,” Aerospace and Environmental Medicine 52(3), 73–79 (2018). [in Russian]
6. P. Wang, J. Wang, L. Wang M. Yin,Y. Li, and J. Wu, “Classification of pathogenic bacteria using near-infrared diffuse reflectance spectroscopy,” Journal of Applied Spectroscopy 85(6), 1029–1036 (2018).
7. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams; J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” Journal of the American Medical Association 316(22), 2402–2410 (2016).
8. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights into Imaging 9, 611–629 (2018).
9. V. O. Vinokurov, Y. Khristoforova, O. Myakinin, I. Bratchenko, A. Moryatov, A. Machikhin, and V. Zakharov, “Neural network classifier of hyperspectral images of skin pathologies,” Computer Optics 45(6), 879–886 (2021). [in Russian]
10. X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Medical Image Analysis 58, 101552 (2019).
11. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis 42, 60–88 (2017).
12. Yu. D. Agafonova, A. V. Gaidel, E. N. Surovtsev, and A. V. Kapishnikov, “Meningioma detection in MR images using convolutional neural network and computer vision methods,” Journal of Biomedical Photonics and Engineering 6(3), 030301 (2020).
13. G. C. Mallika, A. Alsadoon, D. T. H. Pham, S. Abdullah, H. T. Mai, P. W. C. Prasad, and T. Q. V. Nguen, “A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization,” Proceedings of the Institute for System Programming of the Russian Academy of Sciences, 33(2), 93–114 (2021). [In Russian]
14. A. E. Sulavko, P. S. Lozhnikov, A. G. Choban, D. G. Stadnikov, A. A. Nigrey, and D. P. Inivatov, “Evaluation of EEG identification potential using statistical approach and convolutional neural networks,” Information and Control Systems (6), 37–49 (2020).
15. A. Meenakshi, J. A. Ruth, V. R. Kanagavalli, and R. Uma, “Automatic classification of white blood cells using deep features based convolutional neural network,” Multimedia Tools and Applications 81(21), 30121–30142 (2022).
16. H. Kutlu, E. Avci, and F. Özyurt, “White blood cells detection and classification based on regional convolutional neural networks,” Medical Hypotheses 135, 109472 (2020).
17. A. Bodzas, P. Kodytek, and J. Zidek, “Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception,” Frontiers in Bioengineering and Biotechnology 8,1005 (2020).
18. L. Vogado, R. Veras, K. Aires, F. Araújo, R. Silva, M. Ponti, and J. M. R.Tavares, “Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model,” Sensors 21(9), 2989 (2021).
19. C. Marzahl, M. Aubreville, and A. Maier, “Classification of leukemic B-Lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques,” in ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, Lecture Notes in Bioengineering, A. Gupta, R. Gupta (Eds.), Springer, Singapore, 13–22 (2019).
20. M. S. Jarjees, S. S. M. Sheet, and B. T. Ahmed, “Leukocytes identification using augmentation and transfer learning based convolution neural network,” Telkomnika (Telecommunication Computing Electronics and Control) 20(2), 314–320 (2022).
21. S. M. Abas, A. M. Abdulazeez, and D. Q. Zeebaree, “A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia,” Indonesian Journal of Electrical Engineering and Computer Science 25(1), 200–213 (2022).
22. S. A. Naydenov, P. A. Naydenov, and E. V. Shevchenko, “Practical implementation analysis of the fractal dimension calculating algorithm for the medical images by the example of neutrophil nuclei,” Journal of Biomedical Photonics and Engineering 6(1), 010304 (2020).
23. H. Brink, J. W. Richards, and M. Fetherolf, Real-World Machine Learning, Manning Publication, Shelter Island, New York (2016). ISBN 9781617291920.
24. C. M. Bishop, Pattern recognition and machine learning, Springer, New York (2006). ISBN: 9780387310732.
25. A. Smola, S. V. N. Vishwanathan, Introduction to Machine Learning, Cambridge University Press, United Kingdom (2008).
26. M. Nielsen, Neural Networks and Deep Learning, Determination Press, San Francisco, CA, USA (2015).
27. T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. D. Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox and O. Ronneberger, “U-Net: deep learning for cell counting, detection, and morphometry,” Nature Methods 16, 67–70 (2019).
28. T. A. Sumi, M. S. Hossain, and K. Andersson, “Automated Acute Lymphocytic Leukemia (ALL) Detection Using Microscopic Images: An Efficient CAD Approach,” Chapter 5 in Proceedings of Trends in Electronics and Health Informatics: TEHI 2021. Lecture Notes in Networks and Systems 376, M. S. Kaiser, A. Bandyopadhyay, K. Ray, R. Singh, V. Nagar (Eds), 363–376 (2022).
29. T. H. Mamedov, D. V. Dzjuba, and A. N. Narkevich, “Application of convolutional neural networks for recognition of diabetic retinopathy,” Siberian Medical Review 1, 83–87 (2022). [in Russian]
30. A. M. Ignatova, M. A. Zemlyanova, M. S. Stepankov, and Y. V. Kol’dibekova, “Using multifractal analysis to assess the morphology of lung tissues with and without pathology,” Fundamental and Applied Aspects of Public Health Risk Analysis: Materials of the All-Russian Scientific and Practical Internet Conference of Young Scientists and Specialists of Rospotrebnadzor with International Participation, 10–14 October, 2022 Perm, Russia, 222–229 (2022). [in Russian]
31. V. K. Belyakov, E. P. Sukhenko, A. V. Zakharov, P. P. Koltsov, N. V. Kotovich, A. A. Kravchenko, A. S. Kutsaev, A. S. Osipov, and A. B. Kuznetsov, “On one method of blood cell classification and its software implementation,” Software & Systems 4(108), 46–56 (2014). [in Russian]
32. S. N. Rjabceva, V. A. Kovalev, V. D. Malyshev, I. A. Siamionik, M. A. Derevyanko, R. A. Moskalenko, A. S. Dovbysh, T .R. Savchenko, and A. N. Romaniuk, “Development of an algorithm for searching for tumor areas based on the processing of full-slide histological images of breast cancer,” Doklady BGUIR 18(8), 21–28 (2020).
33. J. Pfeil, A. Nechyporenko, M. Frohme, F. T. Hufert, and K. Schulze, “Examination of blood samples using deep learning and mobile microscopy,” BMC Bioinformatics 23, 65 (2022).
34. A. Sharma, B. Buksh, “Intellectual acute lymphoblastic leukemia (ALL) detection model for diagnosis of blood cancer from microscopic images using hybrid convolutional neural network,” International Journal of Engineering and Advanced Technology 8(6), 2972–2981 (2019).
© 2014-2023 Samara National Research University. All Rights Reserved.
Public Media Certificate (RUS). 12+