MALARIA DIAGNOSIS IN MICROSCOPIC BLOOD SMEARS IMAGES: A BRIEF SURVEY
DOI:
https://doi.org/10.65009/ret0n154Keywords:
Malaria Detection, Computer Aided Detection, Plasmodium parasites, Machine Learning, etc.,,Abstract
Malaria has been responsible for the deaths of millions of people all across the world.
As of the year 2021, the World Health Organization (WHO) reported that the disease was
responsible for 6,19,000 deaths worldwide. On account of the fact that plasmodia parasites are
transmitted to people through the bites of mosquitoes, the high death rate that occurs in cases of
malaria is a cause of worry. The sickness is characterized by a high fever that causes shivering,
chills, and headaches, all of which demand a significant amount of effort from the human body.
The death rate is particularly high among youngsters and the elderly. The presence of parasites in
human blood, which can only be discovered seven days after a person has been bitten by a malaria
parasite, is one of the contributing factors that contributes to the high fatality rate. The
administration of antimalarial treatment is not permitted unless it has been established that
parasites are present. This delay in therapy occasionally proves to be fatal for the patient.
Historically, the detection was carried out through the use of the blood smear test, which involved
the examination of a sample of blood through the lens of my microscope. Computer-aided
techniques have been successful in detecting the presence of parasites, which has resulted in a
reduction in the number of errors that are caused by human intervention.
References
. Cox, F. E. G. (2010). History of the discovery of the malaria parasites and their vectors.
Parasites & Vectors, 3(1), 1–9.
. May, Z., Aziz, S. S. A. M., & others. (2013). Automated quantification and classification of
malaria parasites in thin blood smears. 2013 IEEE International Conference on Signal and
Image Processing Applications, 369–373.
. Gilles, H. M. (1991). Management of severe and complicated malaria. A practical handbook.
World Health Organization.
. Murphy, S. C., & Breman, J. G. (2001). Gaps in the childhood malaria burden in Africa:
cerebral malaria, neurological sequelae, anemia, respiratory distress, hypoglycemia, and
complications of pregnancy. The American Journal of Tropical Medicine and Hygiene,
(1_suppl), 57–67.
. Sachs, J., & Malaney, P. (2002). The economic and social burden of malaria. Nature,
(6872), 680–685.
. Vijayalakshmi, A., & Kanna, B. R. (2020). Deep learning approach to detect malaria from
microscopic images. Multimedia Tools and Applications, 79(21), 15297–15317.
. Jan, Z., Khan, A., Sajjad, M., Muhammad, K., Rho, S., & Mehmood, I. (2018). A review on
automated diagnosis of malaria parasite in microscopic blood smears images. Multimedia
Tools and Applications, 77(8), 9801–9826.
. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic
segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, 3431–3440.
. Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2017). Automatic brain tumor
segmentation using cascaded anisotropic convolutional neural networks. International
MICCAI Brainlesion Workshop, 178–190.
. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional
encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 39(12), 2481–2495.
. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for
biomedical image segmentation. International Conference on Medical Image Computing and
Computer-Assisted Intervention, 234–241.
. Dai, J., He, K., & Sun, J. (2016). Instance-aware semantic segmentation via multitask
network cascades. Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, 3150–3158.
. Visin, F., Ciccone, M., Romero, A., Kastner, K., Cho, K., Bengio, Y., Matteucci, M., &
Courville, A. (2016). Reseg: A recurrent neural network-based model for semantic
segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition Workshops, 41–48.
. Das, D K, Maiti, A. K., & Chakraborty, C. (2015). Automated system for characterization
and classification of malaria-infected stages using light microscopic images of thin blood
smears. Journal of Microscopy, 257(3), 238–252.
. Chayadevi, M., & Raju, G. (2014). Usage of art for automatic malaria parasite
identification based on fractal features. Int J Video Image Process Network Sec, 4, 7–15.
. Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J.,
Jaeger, S., & Thoma, G. R. (2018). Pre-trained convolutional neural networks as feature
extractors toward improved malaria parasite detection in thin blood smear images. PeerJ, 6,
e4568.
. Chavan, S. N., & Sutkar, A. M. (2014). Malaria disease identification and analysis using
image processing. International Journal of Computing and Technology (IJCAT), 1(6), 218
. Malihi, L., Ansari-Asl, K., & Behbahani, A. (2013). Malaria parasite detection in giemsa
stained blood cell images. 2013 8th Iranian Conference on Machine Vision and Image
Processing (MVIP), 360–365.
. Seman, N. A., Isa, N. A. M., Li, L. C., Mohamed, Z., Ngah, U. K., & Zamli, K. Z. (2008).
Classification of malaria parasite species based on thin blood smears using multilayer
perceptron network. International Journal of the Computer, the Internet and Management,
(1), 46–52.
. Anggraini, D., Nugroho, A. S., Pratama, C., Rozi, I. E., Pragesjvara, V., & Gunawan, M.
(2011). Automated status identification of microscopic images obtained from malaria thin
blood smears using Bayes decision: a study case in Plasmodium falciparum. 2011
International Conference on Advanced Computer Science and Information Systems, 347
. Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J.,
Jaeger, S., & Thoma, G. R. (2018). Pre-trained convolutional neural networks as feature
extractors toward improved malaria parasite detection in thin blood smear images. PeerJ, 6,
e4568.
. Reni, S. K., Kale, I., & Morling, R. (2015). Analysis of thin blood images for automated
malaria diagnosis. 2015 E-Health and Bioengineering Conference (EHB), 1–4.
. Premalatha V.;Parveen N., "Adaptive fish school search optimized resnet for multi-view
D objects reconstruction", Multimedia Tools and Applications, Volume 83, Year 2024,
Pages 77639-77666. DOI:10.1007/s11042-024-18530-3
. Chandankhede, C., Sachdeo, R. Offline MODI script character recognition using deep
learning
techniques.
Multimed
Tools
https://doi.org/10.1007/s11042-023-14476-0.
. Vickranth V.;Bommareddy S.;Premalatha V., "Application of lean techniques, enterprise
resource planning and artificial intelligence in construction project management",
International Journal of Recent Technology and Engineering, Volume 7, Year 2019, Pages
-153
. Yadav, T., & Sachdeo, R. (2024). Enhanced face age progression and regression model
using hyper-parameter tuning-large scale GAN by hybrid heuristic improvement. The
Imaging
Science
Journal,
