Detecção Automática de Macrófagos em Exame Parasitológico Utilizando Clusterização e Redes Neurais Convolucionais
Resumo
A Leishmaniose Visceral é uma doença parasitária que afeta o sistema de defesa do hospedeiro, sendo os cães, seus principais reservatórios urbanos. A diagnose precoce em animais é crucial para evitar a transmissão para humanos. O padrão ouro para o diagnóstico da doença, o exame parasitológico, é um trabalho repetitivo e fadigante. Este artigo apresenta um sistema para detecção e quantificação de macrófagos em imagens médicas, visando auxiliar o diagnóstico. As regiões de interesse foram segmentadas utilizando o agrupador K-Means e na detecção foi utilizada a arquitetura DenseNet201. A metodologia atingiu 94,7% de Precisão, e 89,4% para o índice de Kappa. Isso indica a capacidade do sistema no auxílio do diagnóstico.
Referências
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Silva, F. S. (2007). Patologia e patogênese da leishmaniose visceral canina. Rev Trop Cienc Agr Biol, 1(1):20–31.
Silva, J. M., Zacarias, D. A., de Figueirêdo, L. C., Soares, M. R. A., Ishikawa, E. A., Costa, D. L., and Costa, C. H. (2014). Bone marrow parasite burden among patients with new world kala-azar is associated with disease severity. The American Journal of Tropical Medicine and Hygiene, 90(4):621.
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Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, volume 31.
Trong Luong, D., Duy Anh, D., Xuan Thang, T., Thi Lan Huong, H., Thuy Hanh, T., and Minh Khanh, D. (2022). Distinguish normal white blood cells from leukemia cells by detection, classification, and counting blood cells using yolov5. In 2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB), pages 156–160.
Xia, R., Wang, P., Chen, R., and Guo, F. (2009). One kind of macrophages images segmentation and labeling method. In 2009 2nd International Congress on Image and Signal Processing, pages 1–5.
Aziz, S., Bilal, M., Khan, M. U., and Amjad, F. (2020). Deep learning-based automatic morphological classification of leukocytes using blood smears. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–5.
Bani-Hani, D., Khan, N., Alsultan, F., Karanjkar, S., and Nagarur, N. (2018). Classification of leucocytes using convolutional neural network optimized through genetic algorithm.
Cook, A. (2017). Using transfer learning to classify images with keras. Retrieved from [link].
da Saúde, M. Situação epidemiológica da leishmaniose visceral.
e Silva, R. R. V., de Araujo, F. H. D., dos Santos, L. M. R., Veras, R. M. S., and de Medeiros, F. N. S. (2016). Optic disc detection in retinal images using algorithms committee with weighted voting. IEEE Latin America Transactions, 14(5):2446–2454.
Eder, K., Kutscher, T., Marzi, A., Barroso, Á., Schnekenburger, J., and Kemper, B. (2021). Automated detection of macrophages in quantitative phase images by deep learning using a mask region-based convolutional neural network. In Label-free Biomedical Imaging and Sensing (LBIS) 2021, volume 11655, pages 88–94. SPIE.
Fleiss, J. L., Levin, B., and Paik, M. C. (2013). Statistical methods for rates and proportions. john wiley & sons.
Gonçalves, C., Andrade, N., Borges, A., Rodrigues, A., Veras, R., Aguiar, B., and Silva, R. (2023). Automatic detection of visceral leishmaniasis in humans using deep learning. Signal, Image and Video Processing, 17(7):3595–3601.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 630–645. Springer.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708.
Liang, G., Hong, H., Xie, W., and Zheng, L. (2018). Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access, 6:36188–36197.
Marcondes, M. and Day, M. J. (2019). Current status and management of canine leishmaniasis in latin america. Research in Veterinary Science, 123:261–272.
Ministério da Saúde, B. (2006). Manual de vigilância e controle da leishmaniose visceral. Série A. Normas e Manuais Técnicos.
Ministério da Saúde. Leishmaniose visceral. Retrieved from [link]. Accessed: 08 mar. 2024.
PAHO (2017). Epidemiological report of the americas. leishmaniases.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 618–626.
Silva, F. S. (2007). Patologia e patogênese da leishmaniose visceral canina. Rev Trop Cienc Agr Biol, 1(1):20–31.
Silva, J. M., Zacarias, D. A., de Figueirêdo, L. C., Soares, M. R. A., Ishikawa, E. A., Costa, D. L., and Costa, C. H. (2014). Bone marrow parasite burden among patients with new world kala-azar is associated with disease severity. The American Journal of Tropical Medicine and Hygiene, 90(4):621.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, volume 31.
Trong Luong, D., Duy Anh, D., Xuan Thang, T., Thi Lan Huong, H., Thuy Hanh, T., and Minh Khanh, D. (2022). Distinguish normal white blood cells from leukemia cells by detection, classification, and counting blood cells using yolov5. In 2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB), pages 156–160.
Xia, R., Wang, P., Chen, R., and Guo, F. (2009). One kind of macrophages images segmentation and labeling method. In 2009 2nd International Congress on Image and Signal Processing, pages 1–5.
Publicado
25/06/2024
Como Citar
BORGES, Armando L.; DIAS, Viviane B. L.; GONÇALVES, Clésio de A.; SOUSA, Lucas B. M. de; VIANA, Daniel de A.; PACHECO, Ana Carolina L.; SILVA, Romuere R. V. e.
Detecção Automática de Macrófagos em Exame Parasitológico Utilizando Clusterização e Redes Neurais Convolucionais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 603-614.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2796.
