Análise Comparativa de Versões YOLO na Detecção e Identificação de Parasitas da Malária
Resumo
A malária é uma doença endêmica causada pelo parasita Plasmodium que pode ser fatal em muitas regiões do mundo. Alguns pesquisadores estão utilizando conceitos de aprendizagem de máquina para detectar e classificar células infectadas pelo parasita Plasmodium. Este trabalho apresenta um estudo comparativo de três versões recentes da rede neural convolucional You Only Look Once (YOLO), são elas a: YOLOv4, Scaled-YOLOv4 e YOLOv5. Foi utilizado a base de dados MP-IDB que possui 210 imagens com o parasita Plasmodium. Os modelos alcançaram excelentes resultados, tendo o melhor resultado com mAP e precisão de 94,8% e 93,3%, respectivamente, para a classificação em dois tipos de espécies do Plasmodium falciparum e vivax.
Referências
Abdurahman, F., Fante, K. A., and Aliy, M. (2021). Malaria parasite detection in thick blood smear microscopic images using modified yolov3 and yolov4 models. BMC bioinformatics, 22(1):1–17.
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Alqudah, A., Alqudah, A. M., and Qazan, S. (2020). Lightweight deep learning for malaria parasite detection using cell-image of blood smear images. Revue d’Intelligence Artificielle, 34(5):571–576.
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Chibuta, S. and Acar, A. C. (2020). Real-time malaria parasite screening in thick blood smears for low-resource setting. Journal of digital imaging, pages 1–13.
Cruz, D., Claro, M., Veras, R., Vogado, L., Portela, H., Moura, N., and Luz, D. (2020). P-fidenet: Plasmodium falciparum identification neural network. In International Symposium on Visual Computing, pages 369–380. Springer.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer.
Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759–8768.
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E. (2012). Annotated high-throughput microscopy image sets for validation. Nature methods, 9(7):637–637.
Loddo, A., Di Ruberto, C., Kocher, M., and Prod’Hom, G. (2018). Mp-idb: The malaria parasite image database for image processing and analysis. In Sipaim–Miccai Biomedical Workshop, pages 57–65. Springer.
Maity, M., Jaiswal, A., Gantait, K., Chatterjee, J., and Mukherjee, A. (2020). Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet. Pattern Recognition Letters, 138:88–94.
Maqsood, A., Farid, M. S., Khan, M. H., and Grzegorzek, M. (2021). Deep malaria parasite detection in thin blood smear microscopic images. Applied Sciences, 11(5):2284.
Nie, Y., Sommella, P., O’Nils, M., Liguori, C., and Lundgren, J. (2019). Automatic detection of melanoma with yolo deep convolutional neural networks. In E-Health and Bioengineering Conference, pages 1–4.
Organization, W. H. et al. (2020). World malaria report 2020: 20 years of global progress and challenges.
Peñas, K. E. D., Rivera, P. T., and Naval, P. C. (2017). Malaria parasite detection and species identification on thin blood smears using a convolutional neural network. In 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, pages 1–6.
Rahman, A., Zunair, H., Reme, T. R., Rahman, M. S., and Mahdy, M. (2021). A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset. Tissue and Cell, 69:101473.
Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., Jaeger, S., and 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.
Ramarolahy, R., Gyasi, E., and Crimi, A. (2020). Classification and generation of microscopy images with plasmodium falciparum via artificial neural networks. bioRxiv.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788.
Somasekar, J., Reddy, B., Reddy, E., and Lai, C. (2011). Computer vision for malaria parasite classification in erythrocytes. International Journal on Computer Science and Engineering, 3(6):2251–2256.
Ünver, H. M. and Ayan, E. (2019). Skin lesion segmentation in dermoscopic images with combination of yolo and grabcut algorithm. Diagnostics, 9(3):72.
Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. M. (2020). Scaled-yolov4: Scaling cross stage partial network. arXiv preprint arXiv:2011.08036.
Wu, D., Lv, S., Jiang, M., and Song, H. (2020). Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Computers and Electronics in Agriculture, 178:105742.
Yang, F., Quizon, N., Yu, H., Silamut, K., Maude, R. J., Jaeger, S., and Antani, S. (2020a). Cascading yolo: automated malaria parasite detection for plasmodium vivax in thin blood smears. In Medical Imaging 2020: Computer-Aided Diagnosis, volume 11314, page 113141Q. International Society for Optics and Photonics.
Yang, G., Feng, W., Jin, J., Lei, Q., Li, X., Gui, G., and Wang, W. (2020b). Face mask recognition system with yolov5 based on image recognition. In 2020 IEEE 6th International Conference on Computer and Communications, pages 1398–1404. IEEE.
Zhou, F., Zhao, H., and Nie, Z. (2021). Safety helmet detection based on yolov5. In 2021 IEEE International Conference on Power Electronics, Computer Applications, pages 6–11.
Al-Masni, M. A., Al-Antari, M. A., Park, J.-M., Gi, G., Kim, T.-Y., Rivera, P., Valarezo, E., Choi, M.-T., Han, S.-M., and Kim, T.-S. (2018). Simultaneous detection and classification of breast masses in digital mammograms via a deep learning yolo-based cad system. Computer methods and programs in biomedicine, 157:85–94.
Alqudah, A., Alqudah, A. M., and Qazan, S. (2020). Lightweight deep learning for malaria parasite detection using cell-image of blood smear images. Revue d’Intelligence Artificielle, 34(5):571–576.
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Chibuta, S. and Acar, A. C. (2020). Real-time malaria parasite screening in thick blood smears for low-resource setting. Journal of digital imaging, pages 1–13.
Cruz, D., Claro, M., Veras, R., Vogado, L., Portela, H., Moura, N., and Luz, D. (2020). P-fidenet: Plasmodium falciparum identification neural network. In International Symposium on Visual Computing, pages 369–380. Springer.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer.
Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759–8768.
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E. (2012). Annotated high-throughput microscopy image sets for validation. Nature methods, 9(7):637–637.
Loddo, A., Di Ruberto, C., Kocher, M., and Prod’Hom, G. (2018). Mp-idb: The malaria parasite image database for image processing and analysis. In Sipaim–Miccai Biomedical Workshop, pages 57–65. Springer.
Maity, M., Jaiswal, A., Gantait, K., Chatterjee, J., and Mukherjee, A. (2020). Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet. Pattern Recognition Letters, 138:88–94.
Maqsood, A., Farid, M. S., Khan, M. H., and Grzegorzek, M. (2021). Deep malaria parasite detection in thin blood smear microscopic images. Applied Sciences, 11(5):2284.
Nie, Y., Sommella, P., O’Nils, M., Liguori, C., and Lundgren, J. (2019). Automatic detection of melanoma with yolo deep convolutional neural networks. In E-Health and Bioengineering Conference, pages 1–4.
Organization, W. H. et al. (2020). World malaria report 2020: 20 years of global progress and challenges.
Peñas, K. E. D., Rivera, P. T., and Naval, P. C. (2017). Malaria parasite detection and species identification on thin blood smears using a convolutional neural network. In 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, pages 1–6.
Rahman, A., Zunair, H., Reme, T. R., Rahman, M. S., and Mahdy, M. (2021). A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset. Tissue and Cell, 69:101473.
Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., Jaeger, S., and 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.
Ramarolahy, R., Gyasi, E., and Crimi, A. (2020). Classification and generation of microscopy images with plasmodium falciparum via artificial neural networks. bioRxiv.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788.
Somasekar, J., Reddy, B., Reddy, E., and Lai, C. (2011). Computer vision for malaria parasite classification in erythrocytes. International Journal on Computer Science and Engineering, 3(6):2251–2256.
Ünver, H. M. and Ayan, E. (2019). Skin lesion segmentation in dermoscopic images with combination of yolo and grabcut algorithm. Diagnostics, 9(3):72.
Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. M. (2020). Scaled-yolov4: Scaling cross stage partial network. arXiv preprint arXiv:2011.08036.
Wu, D., Lv, S., Jiang, M., and Song, H. (2020). Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Computers and Electronics in Agriculture, 178:105742.
Yang, F., Quizon, N., Yu, H., Silamut, K., Maude, R. J., Jaeger, S., and Antani, S. (2020a). Cascading yolo: automated malaria parasite detection for plasmodium vivax in thin blood smears. In Medical Imaging 2020: Computer-Aided Diagnosis, volume 11314, page 113141Q. International Society for Optics and Photonics.
Yang, G., Feng, W., Jin, J., Lei, Q., Li, X., Gui, G., and Wang, W. (2020b). Face mask recognition system with yolov5 based on image recognition. In 2020 IEEE 6th International Conference on Computer and Communications, pages 1398–1404. IEEE.
Zhou, F., Zhao, H., and Nie, Z. (2021). Safety helmet detection based on yolov5. In 2021 IEEE International Conference on Power Electronics, Computer Applications, pages 6–11.
Publicado
15/06/2021
Como Citar
ROCHA, Maura G. R.; VERAS, Rodrigo M. S.; CLARO, Maíla L.; BRITTO NETO, Laurindo S.; AIRES, Kelson R. T..
Análise Comparativa de Versões YOLO na Detecção e Identificação de Parasitas da Malária. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 212-223.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16066.
