Published April 16, 2024
| Version v1
Journal article
Open
An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Creators
- 1. 1,6
- 2. 1,6 & 1,6
- 3. * & *
Description
Sukumarran, Dhevisha, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Ngui, Romano, Sulaiman, Wan Yusoff Wan, Vythilingam, Indra, Divis, Paul Cliff Simon (2024): An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. Parasites & Vectors (188) 17 (1): 1-26, DOI: 10.1186/s13071-024-06215-7, URL: http://dx.doi.org/10.1186/s13071-024-06215-7
Files
source.pdf
Files
(4.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:fc2733d1b33a4e4e3958cacdd415e5f0
|
4.3 MB | Preview Download |
Linked records
Additional details
Identifiers
- LSID
- urn:lsid:plazi.org:pub:FC27FFD1B33A4E4E3958FFCDD415FFF0
References
- 1. WHO.World malaria report 2021.2021. https://www.who.int/teams/ global-malaria-programme/reports/world-malaria-report-2021. Accessed 6 Dec 2021.
- 2. WHO.Global technical strategy for malaria 2016-2030.2021. https:// www.who.int/publications/i/item/9789240031357. Accessed 19 Jul 2021.
- 3. Maturana CR,de Oliveira AD, Nadal S,Bilalli B,Serrat FZ,Soley ME,et al. Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: a review.Front Microbiol. 2022;13:1006659.https://doi.org/10.3389/fmicb.2022.1006659.
- 4. Tangpukdee N, Duangdee C,Wilairatana P,Krudsood S. Malaria diagnosis: a brief review. Korean J Parasitol.2009;47:93-102.https://doi.org/10.3347/ kjp.2009.47.2.93.
- 5. Dian ND, Mohd Salleh AF, Rahim MAFA,Munajat MB,Abd Manap SNA, Ghazali N,et al. Malaria cases in a tertiary hospital in Kuala Lumpur, Malaysia:a 16-year (2005-2020) retrospective review.Trop Med Infect Dis. 2021;6:177.https://doi.org/10.3390/tropicalmed6040177.
- 6. Hussin N, Lim YAL,Goh PP,William T,Jenarun Jelip,Mudin RN. Updates on malaria incidence and profile in Malaysia from 2013 to 2017.Malar J. 2020;19:55.https://doi.org/10.1186/s12936-020-3135-x.
- 7. Chin AZ,Maluda MCM, Jelip J, Jeffree MSB,Culleton R,Ahmed K. Malaria elimination in Malaysia and the rising threat of Plasmodium knowlesi. J Physiol Anthropol.2020;39:36.https://doi.org/10.1186/ s40101-020-00247-5.
- 8. Shambhu S,Koundal D, Das P,Hoang VT,Tran-Trung K,Turabieh H. Computational methods for automated analysis of malaria parasite using blood smear images: recent advances. Comput Intell Neurosci. 2022; 2022:Article 3626726.https://doi.org/10.1155/2022/3626726.
- 9. Quinn J,Munabi IG,Kiwanuka FN. Automated blood smear analysis for mobile malaria diagnosis. In: Karlen W,Iniewski K, editors.Mobile pointof-care monitors and diagnostic device design. Boca Raton: CRC Press; 2014.p. 1-20.
- 10. Silka W,Wieczorek M, Silka J,Wozniak M. Malaria detection using advanced deep learning architecture.Sensors.2023;23:1501. https://doi. org/10.3390/s23031501.
- 11. Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimedia Tools Appl.2021;80:24365-98.https://doi.org/10. 1007/s11042-021-10707-4.
- 12. Andres AI, Leonel MJ,Martha ZD.An overview of deep learning in medical imaging. Inform Med Unlocked.2021;26:100723.https://doi.org/10. 1016/j.imu.2021.100723.
- 13. Shen D,Wu G,Suk HI. Deep learning in medical image analysis.Annu Rev Biomed Eng. 2017;19:221-48. https://doi.org/10.1146/annur ev-bioeng-071516-044442.
- 14. Liang Z,Powell A, Ersoy I, Poostchi M, Silamut K, Palaniappan K,et al. CNN-based image analysis for malaria diagnosis.Proceedings -2016 IEEE International Conference on Bioinformatics and Biomedicine,BIBM 2016; 15-18 Dec 2016,Shenzhen.p. 493-96. https://doi.org/10.1109/BIBM. 2016.7822567
- 15. Rajaraman S,Antani SK, Poostchi M, Silamut K,Hossain MA,Maude RJ, et al. Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images. PeerJ.2018;6:e4568.https://doi.org/10.7717/peerj.4568.
- 16. Rajaraman S,Jaeger S, Antani SK.Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images.PeerJ.2019;7:e6977. https://doi.org/10.7717/PEERJ.6977.
- 17. Kashtriya V,Doegar A, Gupta V,Kashtriya P.Identifying malaria infection in red blood cells using optimized step-increase convolutional neural network model. Int J Innov Technol Explor Eng. 2019;8:813-8.https://doi. org/10.3594/ijitee.I1131.0789S19.
- 18. Sriporn K,Tsai CF,Tsai CE,Wang P. Analyzing malaria disease using effective deep learning approach.Diagnostics.2020;10:744.https://doi.org/10.3390/diagnostics10100744.
- 19. Umer M,Sadiq S,Ahmad M, Ullah S,Choi GS,Mehmood A. A novel stacked CNN for malarial parasite detection in thin blood smear images. IEEE Access.2020;8:93782-92.https://doi.org/10.1109/ACCESS. 2020. 2994810.
- 20. Zhao OS,Kolluri N,Anand A, Chu N, Bhavaraju R,Ojha A,Tik S,Nguyen D, Chen R,Morales A,ValliappanD, Patel JP, Nguyen K et al.Convolutional neural networks to automate the screening of Malaria in low-resource countries.PeerJ. 2020;8:Article e9674. https://doi.org/10.7717/peerj.9674.
- 21. Ragb HK, Dover IT, Ali R.Deep convolutional neural network ensemble for improved malaria parasite detection.Proceedings -Applied Imagery Pattern Recognition Workshop (AIPR),13-15 Oct 2020, Washington DC. p. 1-10.
- 22. Cinar A,Yildirim M. Classification of Malaria cell images with deep learning architectures.Ing Syst Inf.2020;25:35-9.https://doi.org/10.1828/isi. 250105.
- 23. Maqsood A,Farid MS,Khan MH,Grzegorzek M.Deep malaria parasite detection in thin blood smear microscopic images.Appl Sci.2021;11:1-19. https://doi.org/10.3390/app11052284.
- 24. Diyasa IGSM,Fauzi A,Setiawan A,Idhom M,Wahid RR,Alhajir AD. Pre-trained deep convolutional neural network for detecting malaria on the human blood smear images.In:3rd International Conference on Artificial Intelligence in Information and Communication (ICAIIC),20-23 Apr 2021, Jeju Island.p.235-40.
- 25. Loddo A,Fadda C,Di Ruberto C.An empirical evaluation of convolutional networks for Malaria diagnosis.J Imaging.2022;8:66.https://doi.org/10. 3390/jimaging8030066.
- 26. Shambu S,Koundal D,Das P.Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images.Int J Adv Technol Eng Explor.2023;10(105):990-1015.https://doi.org/10.1910/IJATEE. 2023.10101218.
- 27. Vijayalakshmi A,Rajesh KB.Deep learning approach to detect malaria from microscopic images.Multimedia Tools Appl.2020;79:15297-317.https://doi. org/10.1007/s11042-019-7162-y.
- 28. Arshad QA,Ali M,Hassan SU,Chen C,Imran A,Rasul G,et al.A dataset and benchmark for malaria life-cycle classification in thin blood smear images.Neural Comput Appl.2022;34:4473-85.https://doi.org/10.1007/ s00521-021-06602-6.
- 29. Rahman A,Zunair H,Reme TR,Rahman MS,Mahdy MRC.A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset.Tissue Cell.2021;69:101473.https://doi.org/10.1016/j. tice.2020.101473.
- 30. Yang F,Quizon N,Yu H, Silamut K,Maude RJ,St Jaeger,Antani S,et al.Cascading YOLO:automated malaria parasite detection for Plasmodium vivax in thin blood smears.In:Hahn HK,Mazurowski MA,editors.Proc.SPIE 11314, Medical Imaging 2020.Houston:Computer-Aided Diagnosis;2020 Feb 16-19;Houston,Texas,USA;2020.p.11314Q https://doi.org/10.1117/12.25497 01.
- 31. Krishnadas P,Chadaga K,Sampathila N, Rao S,Swathi SK,Prabhu S.Classification of malaria using object detection models.Informatics.2022;9:76. https://doi.org/10.3390/informatics9040076.
- 32. Sukumarran D,Hasikin K,Mohd Khairuddin AS,Ngui R,Wan Sulaiman WY, Vythilingam I, et al.An automated malaria cells detection from thin blood smear images using deep learning.Trop Biomed.2023;40:208-19.https:// doi.org/10.4766/tb.40.2.013.
- 33. Koirala A,Jha M,Bodapati S,Mishra A,Chetty G,Sahu PK,et al.Deep learning for real-time malaria parasite detection and counting using YOLO-mp. IEEE Access.2022;10: 102157-72.https://doi.org/10.1109/ACCESS. 2022. 3208270.
- 34. Zhao ZQ,Zheng P,Xu ST,Wu X.Object detection with deep learning:a review.IEEE Trans Neural Netw Learn Syst.2019;30:3212-32.https://doi.org/ 10.1109/TNNLS.2018.2876865.
- 35. Bochkovskiy A,Wang CY,Liao HYM.YOLOv4:optimal speed and accuracy of object detection.arXiv.2004;10934.https://doi.org/10.4855/arXiv.2004. 10934.
- 36. Khandekar R,Shastry P,Jaishankar S,Faust O,Sampathila N.Automated blast cell detection for acute lymphoblastic leukemia diagnosis.Biomed Signal Process Control.2021;68:102690.https://doi.org/10.1016/j.bspc.2021. 102690.
- 37. Shewajo FA,Fante KA.Tile-based microscopic image processing for malaria screening using a deep learning approach.BMC Med Imaging.2023;23:39. https://doi.org/10.1186/s12880-023-00993-9.
- 38. Albahli S,Nida N,Irtaza A,Yousaf MH,Mahmood MT.Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour. IEEE Access.2020;8:198403-14.https://doi.org/10.1109/ACCESS.2020.30353 45.
- 39. Zhang Z,Li Y,Wu W,Chen H,Cheng L,Wang S.Tumor detection using deep learning method in automated breast ultrasound.Biomed Signal Process Control.2021;68:102677.https://doi.org/10.1016/j. bspc.2021.
- 40. Tian M,Li X,Kong S,Wu L,Yu J.A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot.Front Inform Technol Electron Eng.2022;23:1217-28.https://doi.org/10.1631/FITEE. 2100473.
- 41. Zhang P,Zhong Y,Li X.SlimYOLOv3:narrower,faster and better for real-time UAV Applications.IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).Seoul,South Korea.IEEE;2019.p. 37-45.
- 42. Guo J,Zhang W,Ouyang W.Model compression using progressive channel pruning.IEEE Trans Circuits Syst Video Technol.2021;31:1114-24.https://doi. org/10.1109/TCSVT. 2020.2996231.
- 43. Zhang K,Liu G.Layer pruning for obtaining shallower ResNets.IEEE Signal Process Lett.2022;29:1172-6.https://doi.org/10.1109/LSP.2022.3171128.
- 44. Wu D,Lv S,Jiang M,Song H.Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments.Comput Electron Agric.2021;178:105742.https:// doi.org/10.1016/j.compag.2020.105742.
- 45. Liang X,Jia X,Huang W,He X,Li L,Fan S,et al.Real-time grading of defect apples using semantic segmentation combination with a pruned YOLO V4 network.Foods.2022;11:3150.https://doi.org/10.3390/foods11193150.
- 46. Zhang X,Fan K,Hou H,Liu C.Real-time detection of drones using channel and layer pruning,based on the YOLOv3-SPP3 deep learning algorithm. Micromachines.2022;13:2199.https://doi.org/10.3390/mi13122199.
- 47. Fang L,Wu Y,Li Y,Guo H,Zhang H,Wang X,et al.Using channel and network layer pruning based on deep learning for real-time detection of ginger images.Agriculture.2021;11:1190.https://doi.org/10.3390/agriculture1112 1190.
- 48. Li Y,Wang H,Dang ML,Han D,Moon H,Nguyen TN,et al.A deep learning-based hybrid framework for object detection and recognition in autonomous driving.IEEE Access.2020;8:194228-39.https://doi.org/10.1109/ ACCESS.2020.3033289.
- 49. Chen S,Zhao Q.Shallowing deep networks:layer-wise pruning based on feature representations.IEEE Trans Pattern Anal Mach Intell.2019;41:3048- 56.https://doi.org/10.1109/TPAMI. 2018.2874634.
- 50. Huang Z,Wang N. Data-driven sparse structure selection for deep neural networks.In:Ferrari V,Hebert M,Sminchisescu C,Weiss Y,editors.Lecture notes in computer science.Cham:Springer;2018.p.317-34.
- 51. Veit A,Wilber MJ,Belongie SJ.Residual networks behave like ensembles of relatively shallow networks.In:Lee D,Sugiyama M, Luxburg U,Guyon I Garnett R,editors.Neutral Information Processing System 2016 (NIPS 2016). Barcelona;2016.p.550-8.
- 52. Wang C,Zhou Y,Li J.Lightweight YOLOv4 target detection algorithm fused with ECA mechanism.Processes.2022;10:1285.https://doi.org/10.3390/ pr10071285.
- 53. Yuan DL,Xu Y.Lightweight vehicle detection algorithm based on improved YOLOv4.Eng Lett.2021;29:1544-51.
- 54. Han G,Zhao L,Li Q,Li S,Wang R,Yuan Q,et al.A lightweight algorithm for insulator target detection and defect identification.Sensors.2023;23:1216. https://doi.org/10.3390/s23031216.
- 55. Junos M,Khairuddin A,Thannirmalai S,Dahari M. An optimised YOLObased object detection model for crop harvesting system.IET Image Proc. 2021;15:2112-25.https://doi.org/10.1049/ipr2.12181.
- 56. He K,Zhang X,Ren S,Sun J,Deep residual learning for image recognition.In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas.IEEE;2016:770-8.
- 57. Divis PC,Singh B,Anderios F,Hisam S,Matusop A,Kocken CH,et al. Admixture in humans of two divergent Plasmodium knowlesi populations associated with different macaque host species.PLoS Pathog. 2015;11:e1004888.https://doi.org/10.1371/journal.ppat.1004888.
- 58. Yunos NE,Sharkawi HM,Hii KC,Hu TH,Mohamad DSA,Rosli N, et al.Spatiotemporal distribution and hotspots of Plasmodium knowlesi infections in Sarawak,Malaysian.Borneo Sci Rep.2022;12:17284.https://doi.org/10.1038/ s41598-022-21439-2.
- 59. Divis PCS,Duffy CW,Kadir KA,Singh B,Conway DJ.Genome-wide mosaicism in divergence between zoonotic malaria parasite subpopulations with separate sympatric transmission cycles.Mol Ecol.2018;27:860-70.https:// doi.org/10.1111/mec.14477.
- 60. Daneshvar C,Davis TM,Cox-Singh J,Rafa'ee MZ,Zakaria SK,Divis PC,et al. Clinical and parasitological response to oral chloroquine and primaquine in uncomplicated human Plasmodium knowlesi infections.Malar J.2010;9:238. https://doi.org/10.1186/1475-2875-9-238.
- 61. Hu TH,Rosli N,Mohamad DSA,Kadir KA,Ching ZH,Chai YH,et al.A comparison of the clinical,laboratory and epidemiological features of two divergent subpopulations of Plasmodium knowlesi. Sci Rep.2021;11:20117.https://doi. org/10.1038/s41598-021-99644-8.
- 62. Abdurahman F,Fante KA,Aliy M.Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models. BMC Bioinform.2021;22:112.https://doi.org/10.1186/s12859-021-04036-4.