CLASSIFICATION OF ANALYZABLE METAPHASE IMAGES BY EXTREME LEARNING MACHINES

Yıl 2021, Cilt: 11 Sayı: 1, 78 - 82, 01.06.2021

Abdülkadir Albayrak

https://doi.org/10.36222/ejt.818160

Öz

A chromosome is a DNA molecule that contains the genetic material of an organism. Possible defects in chromosomes can cause structural and functional disorders in living things. Identifying the metaphase stages of cells is a critical step to identify problems in chromosomes. In this proposed study, the discriminative features of possible metaphase images were extracted with Gray Level Co-occurrence Matrix and classified with the Extreme Learning Machines classification method. When the results obtained were evaluated, it was observed that the proposed method was as successful as the deep learning methods in the literature. Especially in recent years, when online learning has become important, the need for re-training of deep learning-based algorithms after each validation will increase the importance of the proposed method in this field. The rapid increase in unlabeled data from each patient every day affects the duration of training and creates time and resource constraints. Fast and accurate modeling of such data with alternative machine learning methods will contribute to the studies in this area.

Anahtar Kelimeler

Karyotyping, metaphase detection, extreme learning machines, gray level co-occurrence matrix

Kaynakça

• Wang, X., Zheng, B., Wood, M., Li, S., Chen, W., & Liu, H. (2005). Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives. Journal of Physics D: Applied Physics, 38(15), 2536.
• Arora, T., & Dhir, R. (2016). A review of metaphase chromosome image selection techniques for automatic karyotype generation. Medical & biological engineering & computing, 54(8), 1147-1157.
• Moazzen, Y., Çapar, A., Albayrak, A., Çalık, N., & Töreyin, B. U. (2019). Metaphase finding with deep convolutional neural networks. Biomedical Signal Processing and Control, 52, 353-361.
• Castleman, K. R. (1992). The PSI automatic metaphase finder. Journal of radiation research, 33(Suppl_1), 124-128.
• Garza-Jinich, M., Rodriguez, C., Corkidi, G., Montero, R., Rojas, E., & Ostrosky-Wegman, P. (1992). A Microcomputer-Based Supervised System for Automatic Scoring of Mitotic Index in Cytotoxicity Studies. In Advances in Machine Vision: Strategies and Applications (pp. 301-311).
• Vrolijk, J., Sloos, W. C. R., Darroudi, F., Natarajan, A. T., & Tanke, H. J. (1994). A system for fluorescence metaphase finding and scoring of chromosomal translocations visualized by in situ hybridization. International journal of radiation biology, 66(3), 287-295.
• McLean, J. R. N., & Johnson, F. (1995). Evaluation of a metaphase chromosome finder: Potential application to chromosome-based radiation dosimetry. Micron, 26(6), 489-492.
• Corkidi, G., Vega, L., Márquez, J., Rojas, E., & Ostrosky-Wegman, P. (1998). Roughness feature of metaphase chromosome spreads and nuclei for automated cell proliferation analysis. Medical and Biological Engineering and Computing, 36(6), 679-685.
• Cosío, F. A., Vega, L., Becerra, A. H., Meléndez, R. P., & Corkidi, G. (2001). Automatic identification of metaphase spreads and nuclei using neural networks. Medical and Biological Engineering and Computing, 39(3), 391-396.
• Wang, X., Li, S., Liu, H., Wood, M., Chen, W. R., & Zheng, B. (2008). Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. Journal of biomedical informatics, 41(2), 264-271.
• Uttamatanin, R., Yuvapoositanon, P., Intarapanich, A., Kaewkamnerd, S., Phuksaritanon, R., Assawamakin, A., & Tongsima, S. (2013). MetaSel: a metaphase selection tool using a Gaussian-based classification technique. BMC bioinformatics, 14(16), 1-13.
• Qiu, Y., Song, J., Lu, X., Li, Y., Zheng, B., Li, S., & Liu, H. (2014). Feature selection for the automated detection of metaphase chromosomes: performance comparison using a receiver operating characteristic method. Analytical Cellular Pathology, 2014.
• Yuchen Qiu, et al., Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation Biophotonics and Immune Responses XI, vol. 9709, 2016.
• Arora, T., & Dhir, R. (2017). An automatic human chromosome metaspread image selection technique. Knowledge and Information Systems, 52(3), 773-790.
• Yilmaz, H., & Turan, M. K. (2017). FahamecV1: A Low Cost Automated Metaphase Detection System. Engineering, Technology & Applied Science Research, 7(6), 2160-2166.
• Yilmaz, H., & Turan, M. K. (2018, May). Filter development for automatic detection of analyzable metaphases. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
• Xu, X., Xu, S., Jin, L., & Song, E. (2011). Characteristic analysis of Otsu threshold and its applications. Pattern recognition letters, 32(7), 956-961.
• Haralick, R. M., & Shapiro, L. G. (1992). Computer and robot vision (Vol. 1, pp. 28-48). Reading: Addison-wesley.
• Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.