Abstract
Biological tissues are the basic components that constitute the structure of all living organisms. There are many types and complex internal components. The effective identification of different types of biological tissues is of great significance for the diagnosis of human diseases and the safe intake of animal-derived foods. Due to the similarities and differences in the elements in different types of biological tissues, this paper focuses on how to extract element characteristics in the same type of tissue for micro-difference identification. Based on the completion of the system construction, in view of the complexity, specificity and high-dimensionality of biological tissue spectral data, combined with machine learning methods, useful information is extracted from complex spectral data and a more robust analysis model is established. And improve the algorithm to improve its analysis performance. Because the surface of biological tissue samples has the characteristics of unevenness, cracks, and softness at room temperature, the experiment needs to precisely control the position of the laser focus relative to the surface of the sample, so as to achieve precise control of the laser pulse direction and laser focusing. The final results of the research show that the spectral recognition accuracy and accuracy of PC1 are 93.61% and 95.65%, respectively, and the spectral recognition accuracy and accuracy of PC2 are 87.56% and 98.46%, respectively. The spectral recognition accuracy can reach more than 85%, and the accuracy rate is %. Can reach more than 90%.
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Wang, H. (2023). Biological Tissue Detection System Based on Improved Optimization Algorithm. In: Xu, Z., Alrabaee, S., Loyola-González, O., Cahyani, N.D.W., Ab Rahman, N.H. (eds) Cyber Security Intelligence and Analytics. CSIA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-031-31775-0_12
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DOI: https://doi.org/10.1007/978-3-031-31775-0_12
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