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Machine learning methods review to detect Chagas disease parasite
DOI:
https://doi.org/10.33064/iycuaa2020803008Keywords:
Chagas disease, Trypanosoma cruzi, detection, segmentation, deep learning, convolutional neural networkAbstract
Chagas disease, caused by the parasite Trypanosoma cruzi, affects many people in Latin America. A blood test is the preferred method to generate a diagnosis of the disease; however, it is a time-consuming process, since it requires a lot of effort by experts to analyze large quantities of samples in the search of the presence of parasites. Implementation of automatic systems that facilitate the detection of the parasite in images of blood samples captured by microscope is very useful. Therefore, in this review article, the different scientific papers that use machine learning techniques to detect and segment the parasite Trypanosoma cruzi in digital images are described.
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References
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• __________ (2014). Algoritmos de segmentación de Trypanosoma cruzi en imágenes de muestras sanguíneas (Tesis de maestría inédita). Universidad Autónoma de Yucatán, Mérida, Yucatán.
• Soberanis-Mukul, R., Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images. Computer Methods and Programs in Biomedicine, 112(3), 633-639. doi:
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• Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). Chagas parasites detection through gaussian discriminant analysis. Abstraction and Application, 8, 6-17. Recuperado de redi. uady.mx:8080/bitstream/handle/123456789/770/UcBrito-
Ruiz_2013.pdf
• __________ (2015). Chagas parasite detection in blood images using adaboost. Computational and Mathematical Methods in Medicine, 2015. doi: 10.1155/2015/139681
• Vapnik, V. N. (1998). Statistical learning theory. Wiley.
• Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEEComputer Society Conference on Computer Vision and Pattern Recognition CVPR 2001. Kauai, HI, US, 511-518. doi:
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• __________ (2020). Chagas disease (also known as American tripanosomiasis) [Datos clave]. Recuperado de https://www.who.int/en/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis)
• Deepa, N., & Chokkalingam, S. P. (2019). Deep convolutional neural networks (CNN) for medical image analysis. International Journal of Engineering and Advanced Technology (IJEAT), 8(3), 607-610. Recuperado de https://www.ijeat.org/wpcontent/uploads/papers/v8i3S/C11290283S19.pdf
• Dong, Y., Jiang, Z., Shen, H., & Pan, W. D. (2017a). Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images. SoutheastCon 2017, Charlotte, NC, US, 1-6. doi: 10.1109/SECON.2017.7925268
• Dong, Y., Jiang, Z., Shen, H., Pan, W. D., Williams, L. A., Reddy, V. V., … Bryan, A. W. (2017b). Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. 2017 IEEE EMBS International Conference on
Biomedical & Health Informatics (BHI), Orlando, Florida, US, 101-104. doi: 10.1109/BHI.2017.7897215
• Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). NY, US: Wiley-Interscience New York.
• Egüez, K. E., Alonso-Padilla, J., Terán, C., Chipana, Z., García, W., Torrico, F., … Pinazo, M. J. (2017). Rapid diagnostic tests duo as alternative to conventional serological assays for conclusive Chagas disease diagnosis. PLoS Neglected Tropical Diseases, 11(4). doi: 10.1371/journal.pntd.0005501
• Gopakumar, G. P., Swetha, M., Sai-Siva, G., & Sai-Subrahmanyam, G. R. K. (2018). Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner. Journal of Biophotonics,
11(3), e201700003. doi: 10.1002/jbio.201700003
• Górriz, M., Aparicio, A., Raventós, B., Vilaplana, V., Sayrol, E., & López-Codina, D. (2018). Leishmaniasis parasite segmentation and classification using deep learning. In F. Perales & J. Kittler (Eds.), Articulated motion and deformable objects (pp.
53-62). Springer. doi: 10.1007/978-3-319-94544-6_6
• Guhl, F. (marzo de 2008). Photograph showing an adult specimen of Triatoma dimidiata from Colombia [Fotografía]. Recuperado de https://es.wikipedia.org/wiki/Archivo:Triatoma_dimidiata-adult.jpg
• Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Thoma, G. (2016). CNN-based image analysis for malaria diagnosis. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 493-496. doi: 10.1109/
BIBM.2016.7822567
• Magoulas, G. D., & Prentza, A. (2001). Machine learning in medical applications. In G. Paliouras, V. Karkaletsis, & C. Spyropoulos (Eds.), Machine Learning and Its Applications (pp. 300-307). doi: 10.1007/3-540-44673-7_19
• MedlinePlus. (s. f.). Examen de sangre ELISA [Especificaciones]. Recuperado de https://medlineplus.gov/spanish/ency/article/003332.htm
• Mehanian, C., Jaiswal, M., Delahunt, C., Thompson, C., Horning, M., Hu, L., … Bell, D. (2017). Computer-automated malaria diagnosis and quantitation using convolutional neural networks. 2017 IEEE International Conference on Computer Vision
Workshops (ICCVW), 116-125. doi: 10.1109/ICCVW.2017.22
• Peñas, K. E. D., Rivera, P. T., & Naval, P. C. (2017). Malaria parasite detection and species identification on thin blood smears using a convolutional neural network. 2017 IEEE/ACM International Conference on Connected Health: Applications,
Systems and Engineering Technologies (CHASE), 1-6. doi: 10.1109/CHASE.2017.51
• Ponce, C., Ponce, E., Vinelli, E., Montoya, A., Aguilar, V., Gonzalez, A., … Silveira, J. (2005). Validation of a rapid and reliable test for diagnosis of Chagas’ disease by detection of Trypanosoma cruzi-specific antibodies in blood of donors and patients in Central America. Journal of Clinical Microbiology, 43(10), 5065 -5068. doi: 10.1128/JCM.43.10.5065-5068.2005
• Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018). Image analysis and machine learning for detecting malaria. Translational Research, 194, 36-55. doi: 10.1016/j.trsl.2017.12.004
• Quinn, J. A., Nakasi, R., Mugagga, P. K. B., Byanyima, P., Lubega, W., & Andama, A. (2016). Deep convolutional neural networks for microscopy-based point of care diagnostics. Proceedings of International Conference on Machine Learning for
Health Care, 56, 271-281. Recuperado de https://arxiv.org/abs/1608.02989
• Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In N. Dey, A. Ashour, & S. Borra (Eds.), Classification in BioApps (pp. 323-350). Springer. doi: 10.1007/978-3-319-65981-7_12
• Ross, N. E., Pritchard, C. J., Rubin, D. M., & Dusé, A. G. (2006). Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing, 44(5), 427-436. doi:
10.1007/s11517-006-0044-2
• Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19(64). doi: 10.1186/s12874-019-0681-4
• Soberanis-Mukul, R. (2012). Detección de Trypanosoma cruzi en imágenes obtenidas a partir de muestras sanguíneas (Tesis de pregrado inédita). Universidad Autónoma de Yucatán, Mérida, Yucatán.
• __________ (2014). Algoritmos de segmentación de Trypanosoma cruzi en imágenes de muestras sanguíneas (Tesis de maestría inédita). Universidad Autónoma de Yucatán, Mérida, Yucatán.
• Soberanis-Mukul, R., Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images. Computer Methods and Programs in Biomedicine, 112(3), 633-639. doi:
10.1016/j.cmpb.2013.07.013
• Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2013). Chagas parasites detection through gaussian discriminant analysis. Abstraction and Application, 8, 6-17. Recuperado de redi. uady.mx:8080/bitstream/handle/123456789/770/UcBrito-
Ruiz_2013.pdf
• __________ (2015). Chagas parasite detection in blood images using adaboost. Computational and Mathematical Methods in Medicine, 2015. doi: 10.1155/2015/139681
• Vapnik, V. N. (1998). Statistical learning theory. Wiley.
• Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEEComputer Society Conference on Computer Vision and Pattern Recognition CVPR 2001. Kauai, HI, US, 511-518. doi:
10.1109/CVPR.2001.990517
• World Health Organization. (s. f.). Chagas disease (American trypanosomiasis) [Ficha informativa]. Recuperado de https://www.who.int/chagas/disease/en/
• __________ (2020). Chagas disease (also known as American tripanosomiasis) [Datos clave]. Recuperado de https://www.who.int/en/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis)
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