The System of Analysis for Mitosis and Meiosis
DOI:
https://doi.org/10.57067/papnah26Keywords:
Deep learning, machine learning, cellular biology, mitosis, meiosis, and image analysisAbstract
In cellular biology, mitosis and meiosis are essential processes that control cell division and genetic transmission. Numerous disciplines, including developmental biology, genetics, and cancer research, depend on an understanding of these processes. An innovative method for examining these processes is the Mitosis and Meiosis Analysis System (MMAS), which uses artificial intelligence (AI) methods for automated image analysis. An overview of MMAS, including its history, elements, and uses, is given in this paper. We go over how to accurately detect, classify, and quantify mitotic and meiotic events from microscopy images by integrating machine learning models, deep learning frameworks, and advanced image processing algorithms into MMAS. In addition, we discuss how MMAS is used in developmental biology, genetics, and cancer research, among other fields of cellular biology study. By automating image analysis and offering insightful information about cellular dynamics, MMAS is a potent tool that can speed up cellular biology research and deepen our understanding of basic biological processes.
References
• Sonneberger, O., Fischer, P., & Brox, T. (2015). References 1. U-Net: Segmenting biomedical images using convolutional networks. In International Conference on Computer-Assisted Intervention and Medical Image Computing, pp. 234-241. Springer, Cham.
• Ferlay, J., Mathers, C., Parkin, D. M., Piñeros, M., & Bray, F. (2019). Colombet, M., Soerjomataram, I., & Mathers, C. GLOBOCAN sources and methods for estimating the incidence and mortality of cancer worldwide in 2018. 1941–1953, International Journal of Cancer, 144(8).
• Huang (2017), Liu (2017), van der Maaten (2017), and Weinberger (2017). convolutional networks with dense connections. In Computer Vision and Pattern Recognition Conference Proceedings, IEEE (pp. 4700–4708).
• 4. Feyzi, Yang, Gholami, and Guo (2020) conducted a study. Review of deep learning for image analysis in histopathology. 67, 101813; Medical Image Analysis.
• Ren, S., Sun, J., Zhang, X., and He, K. (2016). Deep residual learning for the identification of images. In Computer Vision and Pattern Recognition Conference Proceedings, IEEE (pp. 770-778).
• Shlens, J., Ioffe, S., Vanhoucke, V., Szegedy, C., & Wojna, Z. (2016). reevaluating the computer vision inception architecture. In Computer Vision and Pattern Recognition Conference Proceedings, IEEE (pp. 2818-2826).
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