Avaliação de Redes de Segmentação de Deep Learning para Segmentar Melanoma
Abstract
Melanoma is the most serious cause of most deaths among skin cancer types. In addition, its incidence is increasing worldwide, thus showing the importance of cancer detection systems through medical imaging to obtain a faster diagnosis. One of the steps is segmentation, which deals with isolating the injured region. In this research, segmentation methods were compared using different backbones with the U-Net and FPN neural networks. Using the image databases PH2 and DermIS, 0.66 and 0.56 of Dice values were obtained, respectively. Thus, this set can favor the achievement of results closer to state of the art.
References
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Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation.
Santos, E., Veras, R., Miguel, H., Aires, K., Claro, M. L., and Junior, G. B. (2020). A skin lesion semi-supervised segmentation method. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 33–38.
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Universidade do Porto (2021). Ph2 database. Disponível em: https://www.fc.up.pt/addi/ph2%20database.html. Acesso em 15 de outubro de 2021.
Veras, R., Aires, K., Britto, L., et al. (2018). Medical image segmentation using seeded fuzzy c-means: A semi-supervised clustering algorithm. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE.
Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y.-T., Wang, X.-L., and Badami, B. (2020). Skin cancer diagnosis based on optimized convolutional neural network. Articial Intelligence in Medicine, 102:101756.
Al-masni, M. A., Al-antari, M. A., Choi, M.-T., Han, S.-M., and Kim, T.-S. (2018). Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Computer Methods and Programs in Biomedicine, 162:221-231.
Aljanabi, M., Özok, Y. E., Rahebi, J., and Abdullah, A. S. (2018). Skin lesion segmentation method for dermoscopy images using articial bee colony algorithm. Symmetry, 10(8):347.
Cicerone, M. T. and Camp, C. H. (2019). 21 potential roles for spectroscopic coherent raman imaging for histopathology and biomedicine. In Alfano, R. R. and Shi, L., editors, Neurophotonics and Biomedical Spectroscopy, Nanophotonics, pages 547-570. Elsevier.
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC).
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., and Halpern, A. (2018). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic).
Cohen, J. (1968). Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin, 70(4):213.
DermIS (2021). Dermatology information system. Disponível em: https://www.dermis.net/dermisroot/en/home/index.htm. Acesso em 29 de maio de 2021.
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., and Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer.
Gutman, D., Codella, N. C. F., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic).
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report, Citeseer.
Kim, J. U., Kim, H. G., and Ro, Y. M. (2017). Iterative deep convolutional encoder-decoder network for medical image segmentation. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 685–688.
Lin, T., Goyal, P., Girshick, R., He, K., and Dollár, P. (2020). Focal loss for dense IEEE Transactions on Pattern Analysis and Machine Intelligence, object detection. 42(2):318–327.
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection.
Long, J., Shelhamer, E., and Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440.
Mendonça, T., Ferreira, P., Marques, J., Marçal, A., and Rozeira, J. (2013). Ph2 a dermoscopic image database for research and benchmarking. Conference proceedings:2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013:5437–5440.
Ministério da Saúde (2021). Câncer de pele: o que é, causas, sintomas, tratamento e prevenção. Disponível em: [link]. Acesso em 15 de outubro de 2020.
Nan, B. and Mu, Z. (2014). Slic0-based superpixel segmentation method with texture fusion. Chinese Journal of Scientic Instrument, 35(3):527–534.
Nazi, Z. A. and Abir, T. A. (2020). Automatic skin lesion segmentation and melanoma detection: Transfer learning approach with U-Net and dcnn-svm. In Uddin, M. S. and Bansal, J. C., editors, Proceedings of International Joint Conference on Computational Intelligence, pages 371–381, Singapore. Springer Singapore.
Pavel Yakubovskiy (2021). Segmentation models. Disponível em: [link]. Acesso em 30 de maio de 2021.
Provost, F. and Kohavi, R. (1998). Glossary of terms. Journal of Machine Learning, 30(2-3):271–274.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation.
Santos, E., Veras, R., Miguel, H., Aires, K., Claro, M. L., and Junior, G. B. (2020). A skin lesion semi-supervised segmentation method. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 33–38.
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., and van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23(4):501–509.
Universidade do Porto (2021). Ph2 database. Disponível em: https://www.fc.up.pt/addi/ph2%20database.html. Acesso em 15 de outubro de 2021.
Veras, R., Aires, K., Britto, L., et al. (2018). Medical image segmentation using seeded fuzzy c-means: A semi-supervised clustering algorithm. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE.
Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y.-T., Wang, X.-L., and Badami, B. (2020). Skin cancer diagnosis based on optimized convolutional neural network. Articial Intelligence in Medicine, 102:101756.
Published
2021-11-23
How to Cite
SOUZA, Lucas B. M. de; LÉLIS, Samuel Pedro B. D.; SILVA, Romuere R. V..
Avaliação de Redes de Segmentação de Deep Learning para Segmentar Melanoma. In: UNIFIED COMPUTING MEETING OF PIAUÍ (ENUCOMPI), 14. , 2021, Picos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 25-32.
DOI: https://doi.org/10.5753/enucompi.2021.17750.
