Liang, A., Ge, J., Liu, Z., Han, X., Hou, S., Li, G., Liu, M., & Zhao, J. (2025). Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-90679-9
ISO-690 (author-date, English)LIANG, Aohui, GE, Jiaming, LIU, Zhaowei, HAN, Xiangli, HOU, Songtao, LI, Gang, LIU, Ming and ZHAO, Jing, 2025. Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method. Scientific Reports. 1 December 2025. Vol. 15, no. 1, . DOI 10.1038/s41598-025-90679-9.
Modern Language Association 9th editionLiang, A., J. Ge, Z. Liu, X. Han, S. Hou, G. Li, M. Liu, and J. Zhao. “Reliability of Noninvasive Hyperspectral Tongue Diagnosis for Menstrual Diseases Using Machine Learning Method”. Scientific Reports, vol. 15, no. 1, Dec. 2025, https://doi.org/10.1038/s41598-025-90679-9.
Mohr Siebeck - Recht (Deutsch - Österreich)Liang, Aohui/Ge, Jiaming/Liu, Zhaowei/Han, Xiangli/Hou, Songtao/Li, Gang et al.: Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method, Scientific Reports 2025,
Emerald - HarvardLiang, A., Ge, J., Liu, Z., Han, X., Hou, S., Li, G., Liu, M. and Zhao, J. (2025), “Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method”, Scientific Reports, Vol. 15 No. 1, available at:https://doi.org/10.1038/s41598-025-90679-9.