Valentino Sas Henry
Yo'el Pieter Sumihar
Febe Maedjaja



Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.


Anaemia Detection;KNN, Machine Learning;KNN;Yolov8

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World Health Organizatoin, “Anaemia.” Accessed: Dec. 14, 2023. [Online]. Available:

J. P. Radich et al., “Precision Medicine in Low- and Middle-Income Countries,” Annu. Rev. Pathol. Mech. Dis. 2022, vol. 17, pp. 387–402, 2022, doi: 10.1146/annurev-pathol-042320.

GBD 2021 Anaemia Collaborators, “Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021,” Lancet Haematol., vol. 10, no. 9, pp. e713–e734, Sep. 2023, doi: 10.1016/S2352-3026(23)00160-6.

P. Appiahene et al., “Application of ensemble models approach in anemia detection using images of the palpable palm,” Med. Nov. Technol. Devices, vol. 20, Dec. 2023, doi: 10.1016/j.medntd.2023.100269.

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,” Informatics in Medicine Unlocked, vol. 40. Elsevier Ltd, Jan. 01, 2023. doi: 10.1016/j.imu.2023.101283.

P. Appiahene, K. Chaturvedi, J. W. Asare, E. T. Donkoh, and M. Prasad, “CP-AnemiC: A conjunctival pallor dataset and benchmark for anemia detection in children,” Med. Nov. Technol. Devices, vol. 18, Jun. 2023, doi: 10.1016/j.medntd.2023.100244.

M. R. Rizvi, F. M. Alaskar, R. S. Albaradie, N. F. Rizvi, and K. Al-Abdulwahab, “A Novel Non-invasive Technique of Measuring Bilirubin Levels Using BiliCapture,” Oman Med. J., vol. 34, no. 1, pp. 26–33, Jan. 2019, doi: 10.5001/OMJ.2019.05.

R. G. Mannino et al., “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun., vol. 9, no. 1, Dec. 2018, doi: 10.1038/s41467-018-07262-2.

J. W. Asare, P. Appiahene, E. J. Arthur, S. Korankye, S. Afrifa, and E. T. Donkoh, “Detection of anemia using conjunctiva images: A smartphone application approach,” Med. Nov. Technol. Devices, vol. 18, Jun. 2023, doi: 10.1016/j.medntd.2023.100237.

G. Dimauro, M. G. Camporeale, A. Dipalma, A. Guarini, and R. Maglietta, “Anaemia detection based on sclera and blood vessel colour estimation,” Biomed. Signal Process. Control, vol. 81, Mar. 2023, doi: 10.1016/j.bspc.2022.104489.

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,” 2023, doi: 10.22541/au.167570558.82410707/v1.

M. Pulipalupula, S. Patlola, M. Nayaki, M. Yadlapati, J. Das, and B. R. Sanjeeva Reddy, “Object Detection using You only Look Once (YOLO) Algorithm in Convolution Neural Network (CNN),” in 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/I2CT57861.2023.10126213.

Y. Wang, Z. Pan, and J. Dong, “A new two-layer nearest neighbor selection method for kNN classifier,” Knowledge-Based Syst., vol. 235, p. 107604, Jan. 2022, doi: 10.1016/J.KNOSYS.2021.107604.

Asare, Justice Williams; APPIAHENE, PETER; DONKOH, EMMANUEL, “CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana,” Mendeley Data, vol. V1, 2023, doi: 10.17632/m53vz6b7fx.1.

Mohammed Kpannah Fahnbulleh and Xu SHUOBO, “Examining the Usage of Flutter to Design a Yatch in 3D,” Int. J. Res. Stud. Comput. Sci. Eng., vol. 8, no. 1, 2021, doi: 10.20431/2349-4859.0801001.

I. J. Eliza, M. A. Urmi, M. T. T. Anan, M. T. H. Munim, F.-Z.-I. Galib, and A. B. M. A. Al Islam, “eDakterBari: A human-centered solution enabling online medical consultation and information dissemination for resource-constrained communities in Bangladesh,” Heliyon, vol. 10, no. 1, p. e23100, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23100.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40537-019-0197-0.

A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, p. 100258, Dec. 2022, doi: 10.1016/J.ARRAY.2022.100258.

Roboflow, “eye conjunctiva detector Image Dataset.” Accessed: Dec. 19, 2023. [Online]. Available:

encord, “YOLO models for Object Detection Explained [YOLOv8 Updated].” [Online]. Available:

T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9243–9275, Mar. 2023, doi: 10.1007/s11042-022-13644-y.

T. Kumar, A. Mileo, R. Brennan, and M. Bendechache, “Image Data Augmentation Approaches: A Comprehensive Survey and Future directions,” Jan. 2023, [Online]. Available:

S. Uddin, I. Haque, H. Lu, M. A. Moni, and E. Gide, “Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction,” Sci. Rep., vol. 12, no. 1, pp. 1–11, 2022, doi: 10.1038/s41598-022-10358-x.

A. Lee Kwan Yee et al., “Preliminary analysis of rock mass weathering grade using image analysis of CIELAB color space with the validation of Schmidt hammer: A case study,” Phys. Chem. Earth, Parts A/B/C, vol. 129, p. 103291, Feb. 2023, doi: 10.1016/J.PCE.2022.103291.

I. K. Nti, O. Nyarko-Boateng, and J. Aning, “Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation,” Int. J. Inf. Technol. Comput. Sci., vol. 13, no. 6, pp. 61–71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.

H. L. Vu, K. T. W. Ng, A. Richter, and C. An, “Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation,” J. Environ. Manage., vol. 311, p. 114869, Jun. 2022, doi: 10.1016/j.jenvman.2022.114869.

M. Rafało, “Cross validation methods: Analysis based on diagnostics of thyroid cancer metastasis,” ICT Express, vol. 8, no. 2, pp. 183–188, Jun. 2022, doi: 10.1016/j.icte.2021.05.001.

R. Padilla, S. L. Netto, E. A. B. Da Silva, and S. L. Netto, “A Survey on Performance Metrics for Object-Detection Algorithms,” 2020, doi: 10.1109/IWSSIP48289.2020.