Performance Analysis of the Ensemble Model in Anaemia Detection from Unmodified Smartphone-Captured Conjunctiva Images

References

• World Health Organization (WHO), “Definition of Anaemia.” Available: https://www.who.int/topics/anaemia/en/.
   Google Scholar
• World Health Organization (WHO), “Recommended methods to control Anaemia.” Available: https://www.who.int/medicaldevices/initiatives/anaemiacontrol/en/.
   Google Scholar
• World Health Organization (WHO). “Recommended diagnosing methods for Anaemia,” 2008. Available: https://extranet.who.int/rhl/topics/preconceptionpregnancy-childbirth-and-postpartum-care/antenatal-care/who-recommendation-method-diagnosinganaemia-pregnancy.
   Google Scholar
• I. Bates, and J. Critchley. “Haemoglobin colour scale: Operational research agenda and study design.” WHO/EHT/04.18, 2004.
   Google Scholar
• X. Yang, N. Z. Piety, S. M. Vignes, M. S. Benton, J. Kanter, and S. S. Shevkoplyas, “Simple paper-based test for measuring blood hemoglobin concentration in resource-limited settings,” Clin. Chem., Vol. 59, no. 10, pp. 1506–13, 2013. Available: http://clinchem.aaccjnls.org/content/59/10/1506.
   PubMed Web of Science ®Google Scholar
• O. Kim, J. McMurdy, G. Jay, C. Lines, G. Crawford, and M. Alber, “Combined reflectance spectroscopy and stochastic modeling approach for noninvasive hemoglobin determination via palpebral conjunctiva,” Physiol. Rep., Vol. 2, no. 1, pp. e00192, 2014.
   PubMedGoogle Scholar
• G. Dimauro, D. Caivano, and F. Girardi, “A new method and a non-invasive device to estimate anemia based on digital images of the conjunctiva,” IEEE. Access., Vol. 6, pp. 46968–75, 2018.
   Web of Science ®Google Scholar
• V. Bevilacqua, et al., “A novel approach to evaluate blood parameters using computer vision techniques,” in IEEE International Symposium on Medical Measurements and Applications (MeMeA). Benevento: IEEE, 2016.
   Google Scholar
• Y. M. Chen, and S. G. Miaou, “A Kalman filtering and nonlinear penalty regression approach for noninvasive anemia detection with palpebral conjunctiva images,” J. Healthc. Eng., Vol. 2017, pp. 1–11, 2017.
   Web of Science ®Google Scholar
• S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anemia using digital photographs of the conjunctiva,” PLoS One, Vol. 11, no. 4, pp. e0153286, 2016.
   PubMed Web of Science ®Google Scholar
• Y.-M. Chen, S.-G. Miaou, and H. Bian, “Examining palpebral conjunctiva for anemia assessment with image processing methods,” Comput. Methods Programs Biomed., Vol. 137, pp. 125–35, 2016.
   PubMed Web of Science ®Google Scholar
• R. Muthalagu, V. T. Bai, D. Gracias, and S. John, “Developmental screening tool: Accuracy and feasibility of non-invasive anemia estimation,” Technol. Health Care, Vol. 26, no. 4, pp. 723–7, 2018.
   PubMed Web of Science ®Google Scholar
• F. Pedregosa, et al., “Scikit-learn: Machine learning in python,” J. Mach. Learn. Res., Vol. 12, pp. 2825–30, 2011.
   Web of Science ®Google Scholar
• “Towards Data Science.” Available: https://towardsdatascience.com.
   Google Scholar
• R. Glass, R. Batres, C. Selle, and R. Garcia-Ibanez, “The value of simple conjunctival examination in field screening for anemia,” Nutr. Rep. Int., Vol. 21, no. 3, pp. 405–412, 1980.
   Google Scholar
• C. I. Sanchez-Carrillo, T. de Jesus Ramirez-Sanchez, M. Zambrana-Castaneda, and B. J. Selwyn, “Test of a noninvasive instrument for measuring hemoglobin concentration,” Int. J. Technol. Assess. Health Care, Vol. 5, no. 4, pp. 659–67, 1989.
   PubMedGoogle Scholar
• T. N. Sheth, N. K. Choudhry, M. Bowes, and A. S. Detsky, “The relation of conjunctival pallor to the presence of anemia,” J. Gen. Intern. Med., Vol. 12, pp. 102–6, 1997.
   PubMed Web of Science ®Google Scholar
• S. Suner, G. Crawford, J. McMurdy, and G. Jay, “Non-invasive determination of hemoglobin by digital photography of palpebral conjunctiva,” J. Emerg. Med., Vol. 33, no. 2, pp. 105–11, 2007.
   PubMed Web of Science ®Google Scholar
• G. Dimauro, A. Guarini, D. Caivano, F. Girardi, C. Pasciolla, and A. Iacobazzi, “Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva,” IEEE. Access., Vol. 7, pp. 113488–98, 2019.
   Web of Science ®Google Scholar
• E. Allibhai, “Building an ensemble learning model using scikit-learn.” 2018. Available: https://towardsdatascience.com/ensemble-learning-using-scikit-learn-85c4531ff86a.
   Google Scholar
• S. L. Hsieh, et al., “Design ensemble machine learning model for breast cancer diagnosis,” J Med Syst., Vol. 36, pp. 2841–7, 2012.
   PubMed Web of Science ®Google Scholar
• T. Vijayan, M. Sangeetha, A. Kumaravel, and B. Karthik, “Feature selection for simple color histogram filter based on retinal fundus images for diabetic retinopathy recognition,” IETE. J. Res., Vol. 69, pp. 987–94, 2023.
   Google Scholar
• V. Kannagi, A. Jawahar, and V. Nath, “A novel design of epidermal flexible antenna on supraorbital nerve to correlate diabetes and anemia,” IETE. J. Res., Vol. 69, pp. 1190–9, 2023.
   Web of Science ®Google Scholar
• S. Kasiviswanathan, T. B. Vijayan, and S. John, “Ridge regression algorithm based non-invasive anaemia screening using conjunctiva images,” J. Ambient Intell. Humaniz. Comput., 1–11, 2020. https://doi.org/10.1007/s12652-020-02618-3.
   PubMed Web of Science ®Google Scholar
• S. Kasiviswanathan, T. Bai Vijayan, L. Simone, and G. Dimauro, “Semantic segmentation of conjunctiva region for non-invasive anemia detection applications,” Electronics, Vol. 9, pp. 1309, 2020.
   Web of Science ®Google Scholar