Publication Cover
Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 65, 2024 - Issue 3
462
Views
0
CrossRef citations to date
0
Altmetric
Regular Paper

Anemia detection and classification from blood samples using data analysis and deep learning*

ORCID Icon, ORCID Icon &
Pages 1163-1176 | Received 24 Nov 2023, Accepted 02 May 2024, Published online: 14 May 2024

References

  • Asare JW, Appiahene P, Donkoh ET. Detection of anaemia using medical images: a comparative study of machine learning algorithms a systematic literature review. Inf Med Unlocked. 2023;40:1–10. doi:10.1016/j.imu.2023.101283
  • Appiahene P, Asare JW, Donkoh ET, et al. Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData Min. 2023;16(2):1–20.
  • Bahadure NB, Ray AK, Thethi HP. Comparative approach of mri-based brain tumor segmentation and classification using genetic algorithm. J Digit Imaging. 2018;31:477–489. doi:10.1007/s10278-018-0050-6
  • Bahadure NB, Ray AK, Thethi HP. A comparative approach of brain tumor detection using svm, dct and huffman coding in compressed domain. Curr Med Imaging Rev. 3 2018;14:778–787. doi:10.2174/1573405613666170629154727
  • Hemasri A, Sreenidhi MD, Chaitanya VVK, et al. Detection of rbcs, wbcs, platelets count in blood sample by using deep learning. in 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). 2023: 47–51.
  • Gangula Y, KK AM. Detection, classification and counting rbcs and wbcs using deep learning. in 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC), pp. 512-517, 2023.
  • Mumford SL, Towler BP, Pashler AL, et al. Circulating microrna biomarkers in melanoma: tools and challenges in personalised medicine. Biomolecules. 2018;8(2). doi:10.3390/biom8020021
  • Gozdzikiewicz N, Zwolinska D, Polak-Jonkisz D. The use of artificial intelligence algorithms in the diagnosis of urinary tract infectionsmdash;a literature review. J Clin Med. 2022;11(10). doi:10.3390/jcm11102734
  • Chakraborty S, Kansara K, Kumar RD, et al. Non-invasive estimation of clinical severity of anemia using hierarchical ensemble classifiers. J Med Biol Eng. 2022;42:828–838. doi:10.1007/s40846-022-00750-3
  • Al-Salmani K, Abbas HH, Schulpen S, et al. Simplified method for the collection, storage, and comet assay analysis of dna damage in whole blood. Free Radical Biol Med. 2011;51(3):719–725. doi:10.1016/j.freeradbiomed.2011.05.020
  • Schmeiser HH, Muehlbauer K-R, Mier W, et al. Dna damage in human whole blood caused by radiopharmaceuticals evaluated by the comet assay. Mutagenesis. 2019;34(3):239–244. doi:10.1093/mutage/gez007
  • Kavsaoglu AR, Polat K, Hariharan M. Non-invasive prediction of hemoglobin level using machine learning techniques with the ppg signal's characteristics features. Appl Soft Comput. 2015;37:983–991. doi:10.1016/j.asoc.2015.04.008
  • Chandra A, Chauhan A, Bansal N, et al. Application of machine learning in hematological diagnosis. in 2021 International Conference on Technological Advancements and Innovations (ICTAI). 2021:665–671.
  • Nithya R, Nirmala K. Detection of anaemia using image processing techniques from microscopy blood smear images. J Phys Conf Ser. 2022;2318:012043. doi:10.1088/1742-6596/2318/1/012043
  • Waisberg E, Ong J, Zaman N, et al. A non-invasive approach to monitor anemia during long-duration spaceflight with retinal fundus images and deep learning. Life Sci Space Res (Amst). 2022;33:69–71. doi:10.1016/j.lssr.2022.04.004
  • Alomar K, Aysel HI, Cai X. Data augmentation in classification and segmentation: a survey and new strategies. J Imaging. 2023;9(2):1–26. doi:10.3390/jimaging9020046
  • Ahsan MM, Siddique Z. Machine learning-based heart disease diagnosis: a systematic literature review. Artif Intell Med. 2022;128:102289. doi:10.1016/j.artmed.2022.102289
  • Rikan SB, Azar AS, Ghafari A, et al. Covid-19 diagnosis from routine blood tests using artificial intelligence techniques. Biomed Signal Process Control. 2022;72:103263. doi:10.1016/j.bspc.2021.103263
  • Zhao Y, Zhang R, Zhong Y, et al. Statistical analysis and machine learning prediction of disease outcomes for covid-19 and pneumonia patients. Front Cell Infect Microbiol. 2022;12:838749. doi:10.3389/fcimb.2022.838749
  • Asare JW, Appiahene P, Donkoh ET, et al. Iron deficiency anemia detection using machine learning models: a comparative study of fingernails, palm and conjunctiva of the eye images. Eng Rep. 2023;40:1–21.
  • Appiahene P, Arthur EJ, Korankye S, et al. Detection of anemia using conjunctiva images: a smartphone application approach. Med Novel Technol Devices. 2023;18:100237. doi:10.1016/j.medntd.2023.100237
  • Yang X, Piety NZ, Vignes SM, et al. Simple paper-based test for measuring blood hemoglobin concentration in resource-limited settings. Clin Chem. 2013;59(10):1506–1513. doi:10.1373/clinchem.2013.204701
  • Johann KS, Bauer H, Wiegand P, et al. Detecting DNA damage in stored blood samples. Forensic Sci Med Pathol. 2023;19(1):50–59. doi:10.1007/s12024-022-00549-3
  • Vijayarani S, Sudha S. An efficient clustering algorithm for predicting diseases from hemogram blood test samples. Indian J Sci Technol. 2015;8(17):1–8. doi:10.17485/ijst/2015/v8i17/52123
  • Boersma E, Vroegindewey MM, van den Berg VJ, et al. Details on high frequency blood collection, data analysis, available material and patient characteristics in biomarcs. Data Brief. 2019;27:104750. doi:10.1016/j.dib.2019.104750
  • Ahdan S, Setiawansyah S. Android-based geolocation technology on a blood donation system (BDS) using the Dijkstra Algorithm. Int J Appl Inf Technol. 2021;5(1):1–15.
  • Le HT, Nguyen TTL, Nguyen TA, et al. Bloodchain: a blood donation network managed by blockchain technologies. Network. 2022;2(1):21–35. doi:10.3390/network2010002
  • Alhazmi L. Detection of wbc, rbc, and platelets in blood samples using deep learning. BioMed Res Int. 2022;2022(Article ID 1499546):1–10. doi:10.1155/2022/1499546
  • Dimauro G, Griseta ME, Camporeale MG, et al. An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artif Intell Med. 2023;136:102477. doi:10.1016/j.artmed.2022.102477
  • Haggenmuller V, Bogler L, Weber A-C, et al. Smartphone-based point-of-care anemia screening in rural Bihar in India. Commun Med. 2023;3(38):1–10.
  • Dimauro G, Camporeale MG, Dipalma A, et al. Anaemia detection based on sclera and blood vessel colour estimation. Biomed Signal Process Control. 2023;81:104489. doi:10.1016/j.bspc.2022.104489
  • Dhalla S, Maqbool J, Mann TS, et al. Semantic segmentation of palpebral conjunctiva using predefined deep neural architectures for anemia detection. Procedia Comput Sci. 2023;218:328–337. doi:10.1016/j.procs.2023.01.015
  • Saputra DCE, Sunat K, Ratnaningsih T. A new artificial intelligence approach using extreme learning machine as the potentially effective model to predict and analyze the diagnosis of anemia. Healthcare. 2023;11(5):1–25.
  • Kistenev YV, Vrazhnov DA, Shnaider EE, et al. Predictive models for covid-19 detection using routine blood tests and machine learning. Heliyon. 2022;8(10):e11185. doi:10.1016/j.heliyon.2022.e11185
  • Kukar M, Guncar G, Vovko T, et al. Covid-19 diagnosis by routine blood tests using machine learning. Sci Rep. 2021;11:10738. doi:10.1038/s41598-021-90265-9
  • Chen H, Wang F, Su L, et al. Mathematical statistics of factors affecting the unqualified quality of blood samples in medical examination. in 2020 International Conference on Public Health and Data Science (ICPHDS). 2020: 253–256.
  • Pfeil J, Nechyporenko A, Frohme M, et al. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics. 2022;23(65):1–14.
  • Alsheref FK, Gomaa WH. Blood diseases detection using classical machine learning algorithms. Int J Adv Comput Sci Appl. 2019;10(7):77–81. doi:10.14569/IJACSA.2019.0100712
  • Noor NB, Anwar MS, Dey M. An effcient technique of hemoglobin level screening using machine learning algorithms. in 2019 4th International Conference on Electrical Information and Communication Technology (EICT). 2019: 1–6.
  • Golap MA-u, Hashem MMA. Non-invasive hemoglobin concentration measurement using mggp-based model. in 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), pp. 1–6, 2019.
  • Gun_car G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8(1):1–12.
  • Narmatha C, Eljack SM, Tuka AARM, et al. A hybrid fuzzy brainstorm optimization algorithm for the classification of brain tumor MRI images. J Ambient Intell Humaniz Comput. 2020;96(01):867–879.
  • Jiang P, Ergu D, Liu F, et al. A review of yolo algorithm developments. In The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021), pp. 1066–1073, Procedia Computer Science, 2022.