Deep learning in public health: evaluating anemia detection methods

Neelu Jyothi Ahuja; Jyoti Upadhyay; Tanupriya Choudhury; Ashish Jain; Bhupesh Kumar Dewangan; Ketan Kotecha

doi:10.2478/ijssis-2026-0007

References

J. W. Asare, P. Appiahene, and E. T. Donkoh, “Detection of anaemia using medical images: A comparative study of machine learning algorithms – A systematic literature review,” Inform Med Unlocked, vol. 40, p. 101283, 2023, doi: 10.1016/j.imu.2023.101283.
Open DOI Search in Google Scholar Back to article
A. Bhatiasevi, “Anaemia Action Alliance,” World Health Organization, 2015.
Search in Google Scholar Back to article
S. Let, S. Tiwari, A. Singh, and M. Chakrabarty, “Prevalence and determinants of anaemia among women of reproductive age in Aspirational Districts of India: an analysis of NFHS 4 and NFHS 5 data,” BMC Public Health, vol. 24, no. 1, p. 437, Feb. 2024, doi: 10.1186/s12889-024-17789-3.
Open DOI Search in Google Scholar Back to article
B. S. Maner and L. Moosavi, Mean Corpuscular Volume. 2024.
Search in Google Scholar Back to article
S. Gul and M. S. Khan, “A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net,” IEEE Access, vol. 11, pp. 144456–144483, 2023, doi: 10.1109/ACCESS.2023.3344813.
Open DOI Search in Google Scholar Back to article
A. Gupta et al., “Risk factors of anemia amongst elderly population living at high-altitude region of India,” J Family Med Prim Care, vol. 9, no. 2, p. 673, 2020, doi: 10.4103/jfmpc.jfmpc_468_19.
Open DOI Search in Google Scholar Back to article
S. K. Singh, H. Lhungdim, C. Shekhar, L. K. Dwivedi, S. Pedgaonkar, and K. S. James, “Key drivers of reversal of trend in childhood anaemia in India: evidence from Indian demographic and health surveys, 2016–21,” BMC Public Health, vol. 23, no. 1, p. 1574, Aug. 2023, doi: 10.1186/s12889-023-16398-w.
Open DOI Search in Google Scholar Back to article
P. Appiahene et al., “Application of ensemble models approach in anemia detection using images of the palpable palm,” Med Nov Technol Devices, vol. 20, p. 100269, Dec. 2023, doi: 10.1016/j.medntd.2023.100269.
Open DOI Search in Google Scholar Back to article
M. Zuin, G. Rigatelli, L. Quadretti, L. Fogato, G. Zuliani, and L. Roncon, “Prognostic Role of Anemia in COVID-19 Patients: A Meta-Analysis,” Infect Dis Rep, vol. 13, no. 4, pp. 930–937, Oct. 2021, doi: 10.3390/idr13040085.
Open DOI Search in Google Scholar Back to article
R. Bellmann-Weiler et al., “Prevalence and Predictive Value of Anemia and Dysregulated Iron Homeostasis in Patients with COVID-19 Infection,” J Clin Med, vol. 9, no. 8, p. 2429, Jul. 2020, doi: 10.3390/jcm9082429.
Open DOI Search in Google Scholar Back to article
S. Dhalla et al., “Semantic segmentation of palpebral conjunctiva using predefined deep neural architectures for anemia detection,” Procedia Comput Sci, vol. 218, pp. 328–337, 2023, doi: 10.1016/j.procs.2023.01.015.
Open DOI Search in Google Scholar Back to article
P. T. Dalvi and N. Vernekar, “Anemia detection using ensemble learning techniques and statistical models,” in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, May 2016, pp. 1747–1751. doi: 10.1109/RTEICT.2016.7808133.
Open DOI Search in Google Scholar Back to article
J. W. Asare, P. Appiahene, E. T. Donkoh, and G. Dimauro, “Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images,” Engineering Reports, vol. 5, no. 11, Nov. 2023, doi: 10.1002/eng2.12667.
Open DOI Search in Google Scholar Back to article
J. W. Asare, W. L. Brown-Acquaye, M. M. Ujakpa, E. Freeman, and P. Appiahene, “Application of machine learning approach for iron deficiency anaemia detection in children using conjunctiva images,” Inform Med Unlocked, vol. 45, p. 101451, 2024, doi: 10.1016/j.imu.2024.101451.
Open DOI Search in Google Scholar Back to article
P. Appiahene, J. W. Asare, E. T. Donkoh, G. Dimauro, and R. Maglietta, “Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms,” BioData Min, vol. 16, no. 1, p. 2, Jan. 2023, doi: 10.1186/s13040-023-00319-z.
Open DOI Search in Google Scholar Back to article
A. Kesarwani, S. Das, D. R. Kisku, and M. Dalui, “Dual mode information fusion with pre-trained CNN models and transformer for video-based non-invasive anaemia detection,” Biomed Signal Process Control, vol. 88, p. 105592, Feb. 2024, doi: 10.1016/j.bspc.2023.105592.
Open DOI Search in Google Scholar Back to article
El-Sayed M. Towfek El-kenawy, “A Machine Learning Model for Hemoglobin Estimation and Anemia Classification,” in International Journal of Computer Science and Information Security, International Journal of Computer Science and Information Security, Feb. 2019, pp. 100–108.
Search in Google Scholar Back to article
J. R. Khan, S. Chowdhury, H. Islam, and E. Raheem, “Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh,” Journal of Data Science, vol. 17, no. 1, pp. 195–218, Feb. 2021, doi: 10.6339/JDS.201901_17(1)0.0009.
Open DOI Search in Google Scholar Back to article
M. Jaiswal, A. Srivastava, and T. J. Siddiqui, “Machine Learning Algorithms for Anemia Disease Prediction,” 2019, pp. 463–469. doi: 10.1007/978-981-13-2685-1_44.
Open DOI Search in Google Scholar Back to article
S. KILICARSLAN, M. CELIK, and Ş. SAHIN, “Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification,” Biomed Signal Process Control, vol. 63, p. 102231, Jan. 2021, doi: 10.1016/j.bspc.2020.102231.
Open DOI Search in Google Scholar Back to article
Y. Chen, K. Zhong, Y. Zhu, and Q. Sun, “Two-stage hemoglobin prediction based on prior causality,” Front Public Health, vol. 10, Nov. 2022, doi: 10.3389/fpubh.2022.1079389.
Open DOI Search in Google Scholar Back to article
S. M. Sarsam, H. Al-Samarraie, A. I. Alzahrani, and A. S. Shibghatullah, “A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia,” Artif Intell Med, vol. 134, p. 102428, Dec. 2022, doi: 10.1016/j.artmed.2022.102428.
Open DOI Search in Google Scholar Back to article
S. Yeruva, M. S. Varalakshmi, B. P. Gowtham, Y. H. Chandana, and PESN. K. Prasad, “Identification of Sickle Cell Anemia Using Deep Neural Networks,” Emerging Science Journal, vol. 5, no. 2, pp. 200–210, Apr. 2021, doi: 10.28991/esj-2021-01270.
Open DOI Search in Google Scholar Back to article
A. Tamir et al., “Detection of anemia from image of the anterior conjunctiva of the eye by image processing and thresholding,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), IEEE, Dec. 2017, pp. 697–701. doi: 10.1109/R10-HTC.2017.8289053.
Open DOI Search in Google Scholar Back to article
M. Wang et al., “Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses,” Neurocomputing, vol. 267, pp. 69–84, Dec. 2017, doi: 10.1016/j.neucom.2017.04.060.
Open DOI Search in Google Scholar Back to article
M. Wang and H. Chen, “Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis,” Appl Soft Comput, vol. 88, p. 105946, Mar. 2020, doi: 10.1016/j.asoc.2019.105946.
Open DOI Search in Google Scholar Back to article
M. Yang, P. Kumar, J. Bhola, and M. Shabaz, “Development of image recognition software based on artificial intelligence algorithm for the efficient sorting of apple fruit,” International Journal of System Assurance Engineering and Management, vol. 13, no. S1, pp. 322–330, Mar. 2022, doi: 10.1007/s13198-021-01415-1.
Open DOI Search in Google Scholar Back to article
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
Open DOI Search in Google Scholar Back to article
D. T. B. I. D. and J. D. V. Provan, Oxford Handbook of Clinical Haematology, 4th ed. Uk: Oxford University press, 2015.
Search in Google Scholar Back to article
J.-B. Michel and J. L. Martin-Ventura, “Red Blood Cells and Hemoglobin in Human Atherosclerosis and Related Arterial Diseases,” Int J Mol Sci, vol. 21, no. 18, p. 6756, Sep. 2020, doi: 10.3390/ijms21186756.
Open DOI Search in Google Scholar Back to article
B. Savkovic et al., “Comparative Characteristics of Ductile Iron and Austempered Ductile Iron Modeled by Neural Network,” Materials, vol. 12, no. 18, p. 2864, Sep. 2019, doi: 10.3390/ma12182864.
Open DOI Search in Google Scholar Back to article
M. Duan, K. Li, X. Liao, and K. Li, “A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 6, pp. 2337–2351, Jun. 2018, doi: 10.1109/TNNLS.2017.2654357.
Open DOI Search in Google Scholar Back to article
T. Yoshida, M. Prudent, and A. D'alessandro, “Red blood cell storage lesion: causes and potential clinical consequences.,” Blood Transfus, vol. 17, no. 1, pp. 27–52, Jan. 2019, doi: 10.2450/2019.0217-18.
Open DOI Search in Google Scholar Back to article
A. R. Andrade, L. H. S. Vogado, R. de M. S. Veras, R. R. V. Silva, F. H. D. Araujo, and F. N. S. Medeiros, “Recent computational methods for white blood cell nuclei segmentation: A comparative study,” Comput Methods Programs Biomed, vol. 173, pp. 1–14, May 2019, doi: 10.1016/j.cmpb.2019.03.001.
Open DOI Search in Google Scholar Back to article
J. C. Chavez, C. Bachmeier, and M. A. Kharfan-Dabaja, “CAR T-cell therapy for B-cell lymphomas: clinical trial results of available products,” Ther Adv Hematol, vol. 10, p. 204062071984158, Jan. 2019, doi: 10.1177/2040620719841581.
Open DOI Search in Google Scholar Back to article
L. Jiang, C. Tang, and H. Zhou, “White blood cell classification via a discriminative region detection assisted feature aggregation network,” Biomed Opt Express, vol. 13, no. 10, p. 5246, Oct. 2022, doi: 10.1364/BOE.462905.
Open DOI Search in Google Scholar Back to article
X. Han, C. Wang, and Z. Liu, “Red Blood Cells as Smart Delivery Systems,” Bioconjug Chem, vol. 29, no. 4, pp. 852–860, Apr. 2018, doi: 10.1021/acs.bioconjchem.7b00758.
Open DOI Search in Google Scholar Back to article
Q. Xia, Y. Zhang, Z. Li, X. Hou, and N. Feng, “Red blood cell membrane-camouflaged nanoparticles: a novel drug delivery system for antitumor application,” Acta Pharm Sin B, vol. 9, no. 4, pp. 675–689, Jul. 2019, doi: 10.1016/j.apsb.2019.01.011.
Open DOI Search in Google Scholar Back to article
L. Guo and M. T. Rondina, “The Era of Thromboinflammation: Platelets Are Dynamic Sensors and Effector Cells During Infectious Diseases,” Front Immunol, vol. 10, Sep. 2019, doi: 10.3389/fimmu.2019.02204.
Open DOI Search in Google Scholar Back to article
A. W. Anz, R. Hubbard, N. K. Rendos, P. A. Everts, J. R. Andrews, and J. G. Hackel, “Bone Marrow Aspirate Concentrate Is Equivalent to Platelet-Rich Plasma for the Treatment of Knee Osteoarthritis at 1 Year: A Prospective, Randomized Trial,” Orthop J Sports Med, vol. 8, no. 2, p. 232596711990095, Feb. 2020, doi: 10.1177/2325967119900958.
Open DOI Search in Google Scholar Back to article
C. Crotti, E. Agape, A. Becciolini, M. Biggioggero, and E. G. Favalli, “Targeting Granulocyte-Monocyte Colony-Stimulating Factor Signaling in Rheumatoid Arthritis: Future Prospects,” Drugs, vol. 79, no. 16, pp. 1741–1755, Nov. 2019, doi: 10.1007/s40265-019-01192-z.
Open DOI Search in Google Scholar Back to article
C. Silvestre-Roig, Z. G. Fridlender, M. Glogauer, and P. Scapini, “Neutrophil Diversity in Health and Disease,” Trends Immunol, vol. 40, no. 7, pp. 565–583, Jul. 2019, doi: 10.1016/j.it.2019.04.012.
Open DOI Search in Google Scholar Back to article
P. B. Narasimhan, P. Marcovecchio, A. A. J. Hamers, and C. C. Hedrick, “Nonclassical Monocytes in Health and Disease,” Annu Rev Immunol, vol. 37, no. 1, pp. 439–456, Apr. 2019, doi: 10.1146/annurev-immunol-042617-053119.
Open DOI Search in Google Scholar Back to article
B. V. Rooney, A. B. Bigley, E. C. LaVoy, M. Laughlin, C. Pedlar, and R. J. Simpson, “Lymphocytes and monocytes egress peripheral blood within minutes after cessation of steady state exercise: A detailed temporal analysis of leukocyte extravasation,” Physiol Behav, vol. 194, pp. 260–267, Oct. 2018, doi: 10.1016/j.physbeh.2018.06.008.
Open DOI Search in Google Scholar Back to article
J. Florentin et al., “Inflammatory Macrophage Expansion in Pulmonary Hypertension Depends upon Mobilization of Blood-Borne Monocytes,” The Journal of Immunology, vol. 200, no. 10, pp. 3612–3625, May 2018, doi: 10.4049/jimmunol.1701287.
Open DOI Search in Google Scholar Back to article
A. D. Klion, S. J. Ackerman, and B. S. Bochner, “Contributions of Eosinophils to Human Health and Disease,” Annual Review of Pathology: Mechanisms of Disease, vol. 15, no. 1, pp. 179–209, Jan. 2020, doi: 10.1146/annurev-pathmechdis-012419-032756.
Open DOI Search in Google Scholar Back to article
K. Nakagome and M. Nagata, “Involvement and Possible Role of Eosinophils in Asthma Exacerbation,” Front Immunol, vol. 9, Sep. 2018, doi: 10.3389/fimmu.2018.02220.
Open DOI Search in Google Scholar Back to article
G. E. Idos, J. Kwok, N. Bonthala, L. Kysh, S. B. Gruber, and C. Qu, “The Prognostic Implications of Tumor Infiltrating Lymphocytes in Colorectal Cancer: A Systematic Review and Meta-Analysis,” Sci Rep, vol. 10, no. 1, p. 3360, Feb. 2020, doi: 10.1038/s41598-020-60255-4.
Open DOI Search in Google Scholar Back to article
S. Gavrilov, K. Zhudenkov, G. Helmlinger, J. Dunyak, K. Peskov, and S. Aksenov, “Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients with Advanced Non-Small Cell Lung Cancer Treated with Durvalumab,” CPT Pharmacometrics Syst Pharmacol, vol. 10, no. 1, pp. 67–74, Jan. 2021, doi: 10.1002/psp4.12578.
Open DOI Search in Google Scholar Back to article
A. D. Cohen et al., “B cell maturation antigen–specific CAR T cells are clinically active in multiple myeloma,” Journal of Clinical Investigation, vol. 129, no. 6, pp. 2210–2221, Apr. 2019, doi: 10.1172/JCI126397.
Open DOI Search in Google Scholar Back to article

Deep learning in public health: evaluating anemia detection methods

References

Paradigm

My account