Computação de Borda versus Computação em Nuvem: Impacto do Pré-processamento de Imagens de Retinas
Resumo
Na área da saúde, a prevenção é uma forma eficaz de evitar a progressão de doenças, muitas das quais podem ser tratadas quando diagnosticadas precocemente. A procura por exames preventivos tem aumentado e não se consegue atender essa procura com eficiência. Logo, existe a necessidade de automatizar e aumentar a eficiência de exames de triagem. Entretanto, a captura de dados para estes sistemas geralmente utiliza vários dispositivos de hardware sob condições ambientais diversas, induzindo ruído nos dados. Portanto, antes da fase de triagem, a seleção de uma estrutura de pré-processamento eficaz é fundamental. Neste artigo, é discutido o desenvolvimento de uma aplicação para pré-processamento de imagens de retinas para uso eficiente em sistemas de triagem e o impacto que o pré-processamento causa na interconexão de rede. Foi reduzido em até ≈ 73% o tempo de execução com a versão paralela. Também foi reduzido em ≈ 11,5× a largura de banda utilizada, alcançando taxa de transferência acima de 5 imagens/segundo com pré-processamento na Borda, 2,57× maior do que na Nuvem.
Referências
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Jaafar, H. F., Nandi, A. K., and Al-Nuaimy, W. (2011). Detection of exudates from digital fundus images using a region-based segmentation technique. In 2011 19th European signal processing conference, pages 1020-1024. IEEE.
Janghorbani, M., Jones, R. B., and Allison, S. P. (2000). Incidence of and risk factors for proliferative retinopathy and its association with blindness among diabetes clinic attenders. Ophthalmic Epidemiology, 7(4):225-241.
Lechner, J., O'Leary, O. E., and Stitt, A. W. (2017). The pathology associated with diabetic retinopathy. Vision research, 139:7-14.
Lee, S. J., McCarty, C. A., Taylor, H. R., and Keeffe, J. E. (2001). Costs of mobile screening for diabetic retinopathy: a practical framework for rural populations. Australian Journal of Rural Health, 9(4):186-192.
Luo, F., Dong, L., and Jia, F. (2010). Method and implementation of building forces protocol dissector based on wireshark. In 2010 2nd IEEE International Conference on Information Management and Engineering, pages 291-294. IEEE.
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Pires, R. and Rocha, A. (2011). Combinação de classificadores para um sistema automático de triagem de retinopatia diabética. In Proceedings of the SPS 2011.
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., et al. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas. Diabetes research and clinical practice, 157:107843.
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Solomon, T., Zungeru, A. M., and Selvaraj, R. (2016). Network traffic monitoring in an industrial environment. In 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), pages 133-139. IEEE.
Stitt, A. W., Curtis, T. M., Chen, M., Medina, R. J., McKay, G. J., Jenkins, A., Gardiner, T. A., Lyons, T. J., Hammes, H.-P., Simo, R., et al. (2016). The progress in understanding and treatment of diabetic retinopathy. Progress in retinal and eye research, 51:156-186.
Swathi, C., Anoop, B., Dhas, D. A. S., and Sanker, S. P. (2017). Comparison of different image preprocessing methods used for retinal fundus images. In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), pages 175-179. IEEE.
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Vandarkuzhali, T., Ravichandran, C., and Preethi, D. (2013). Detection of exudates caused by diabetic retinopathy in fundus retinal image using fuzzy k means and neural network. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) eISSN, pages 2278-1676.
Voets, M., Møllersen, K., and Bongo, L. A. (2019). Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PloS one, 14(6):e0217541.
Waheed, N. K. (2018). Proliferative diabetic retinopathy. In Atlas of Retinal OCT: Optical Coherence Tomography, pages 88-89. Elsevier.
Wang, X.-N., Dai, L., Li, S.-T., Kong, H.-Y., Sheng, B., and Wu, Q. (2020). Automatic grading system for diabetic retinopathy diagnosis using deep learning artificial intelligence software. Current Eye Research, 45(12):1550-1555.
Wilkinson, C., Ferris III, F. L., Klein, R. E., Lee, P. P., Agardh, C. D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J. T., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9):1677-1682.
Wong, T. Y., Klein, R., Islam, F. A., Cotch, M. F., Folsom, A. R., Klein, B. E., Sharrett, A. R., Shea, S., of Atherosclerosis (MESA, M.-E. S., et al. (2006). Diabetic retinopathy in a multi-ethnic cohort in the united states. American journal of ophthalmology, 141(3):446-455.
Ying-hua, L., Bing-Ru, Y., Dan-yang, C., and Nan, M. (2011). State-of-the-art in distributed privacy preserving data mining. In 2011 IEEE 3rd International Conference on Communication Software and Networks, pages 545-549. IEEE.
Asrodia, P. and Patel, H. (2012). Network traffic analysis using packet sniffer. International journal of engineering research and applications, 2(3):854-856.
Chatterjee, A., Datta, N. S., Dutta, H. S., Majumder, K., and Chatterjee, S. (2021). A study on retinal image preprocessing methods for the automated diabetic retinopathy screening operation. Applications of Artificial Intelligence and Machine Learning, pages 375-384.
Goyal, P. and Goyal, A. (2017). Comparative study of two most popular packet sniffing tools-tcpdump and wireshark. In 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), pages 77-81. IEEE.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402-2410.
Jaafar, H. F., Nandi, A. K., and Al-Nuaimy, W. (2011). Detection of exudates from digital fundus images using a region-based segmentation technique. In 2011 19th European signal processing conference, pages 1020-1024. IEEE.
Janghorbani, M., Jones, R. B., and Allison, S. P. (2000). Incidence of and risk factors for proliferative retinopathy and its association with blindness among diabetes clinic attenders. Ophthalmic Epidemiology, 7(4):225-241.
Lechner, J., O'Leary, O. E., and Stitt, A. W. (2017). The pathology associated with diabetic retinopathy. Vision research, 139:7-14.
Lee, S. J., McCarty, C. A., Taylor, H. R., and Keeffe, J. E. (2001). Costs of mobile screening for diabetic retinopathy: a practical framework for rural populations. Australian Journal of Rural Health, 9(4):186-192.
Luo, F., Dong, L., and Jia, F. (2010). Method and implementation of building forces protocol dissector based on wireshark. In 2010 2nd IEEE International Conference on Information Management and Engineering, pages 291-294. IEEE.
Network, S. I. G. (2010). Management of obesity: a national clinical guideline. Scottish Intercollegiate Guidelines Network: Edinburgh, 20.
Pires, R. and Rocha, A. (2011). Combinação de classificadores para um sistema automático de triagem de retinopatia diabética. In Proceedings of the SPS 2011.
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., et al. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas. Diabetes research and clinical practice, 157:107843.
Servier, M. A. (2022). Eyes archives servier medical art. [Accessed Jun. 1, 2022].
Solomon, T., Zungeru, A. M., and Selvaraj, R. (2016). Network traffic monitoring in an industrial environment. In 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), pages 133-139. IEEE.
Stitt, A. W., Curtis, T. M., Chen, M., Medina, R. J., McKay, G. J., Jenkins, A., Gardiner, T. A., Lyons, T. J., Hammes, H.-P., Simo, R., et al. (2016). The progress in understanding and treatment of diabetic retinopathy. Progress in retinal and eye research, 51:156-186.
Swathi, C., Anoop, B., Dhas, D. A. S., and Sanker, S. P. (2017). Comparison of different image preprocessing methods used for retinal fundus images. In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), pages 175-179. IEEE.
Tirumala, A., Qin, F., Dugan, J., Ferguson, J., and Gibbs, K. (2005). Iperf: the tcp/udp bandwidth measurement tool (2005). URL: http://iperf.sourceforge.net.
Vandarkuzhali, T., Ravichandran, C., and Preethi, D. (2013). Detection of exudates caused by diabetic retinopathy in fundus retinal image using fuzzy k means and neural network. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) eISSN, pages 2278-1676.
Voets, M., Møllersen, K., and Bongo, L. A. (2019). Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PloS one, 14(6):e0217541.
Waheed, N. K. (2018). Proliferative diabetic retinopathy. In Atlas of Retinal OCT: Optical Coherence Tomography, pages 88-89. Elsevier.
Wang, X.-N., Dai, L., Li, S.-T., Kong, H.-Y., Sheng, B., and Wu, Q. (2020). Automatic grading system for diabetic retinopathy diagnosis using deep learning artificial intelligence software. Current Eye Research, 45(12):1550-1555.
Wilkinson, C., Ferris III, F. L., Klein, R. E., Lee, P. P., Agardh, C. D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J. T., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9):1677-1682.
Wong, T. Y., Klein, R., Islam, F. A., Cotch, M. F., Folsom, A. R., Klein, B. E., Sharrett, A. R., Shea, S., of Atherosclerosis (MESA, M.-E. S., et al. (2006). Diabetic retinopathy in a multi-ethnic cohort in the united states. American journal of ophthalmology, 141(3):446-455.
Ying-hua, L., Bing-Ru, Y., Dan-yang, C., and Nan, M. (2011). State-of-the-art in distributed privacy preserving data mining. In 2011 IEEE 3rd International Conference on Communication Software and Networks, pages 545-549. IEEE.
Publicado
19/10/2022
Como Citar
KÜNAS, Cristiano A.; PINTO, Dayla R.; NAVAUX, Philippe O. A.; GRANVILLE, Lisandro Z..
Computação de Borda versus Computação em Nuvem: Impacto do Pré-processamento de Imagens de Retinas. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 23. , 2022, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 85-96.
DOI: https://doi.org/10.5753/wscad.2022.226325.
