Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning

  • Jhonatan Souza UFPR
  • Tiago de Oliveira UFPR
  • Claudemir Casa UFPR
  • André Ortoncelli UFPR/UTFPR


This work presents an approach to the automatic detection of Butterfly Malar Rash (BMR) in images. BMR is a Lupus symptom characterized by a reddish facial rash that appears symmetrically in the cheeks and the back of the nose. The proposed approach is based on Transfer Learning, a popular approach in Deep Learning that consists in the use of pre-trained models as the starting point for computer vision and natural language processing tasks. To perform the experiments, a database was created with images manually collected from the Instagram social network, searching for images with #butterflyrash. We evaluated the proposed approach with eight Convolutional Neural Networks (CNN) architecture. The experimental results are good results, with a precision of up to 0.957.

Palavras-chave: Lupus diagnosis, Skin lesions, Deep Learning, Computer Vision


V. P. Bernardes, L. D. B. Oliveira, and C. Marcon, “Lupus eritematoso sistêmico juvenil: Diagnóstico de doença crônica e dinâmica familiar,” Barbarói, pp. 75–90, 2011.

D. J. Wallace, The lupus book: A guide for patients and their families. Oxford University Press, 2019.

C. Yu, M. E. Gershwin, and C. Chang, “Diagnostic criteria for systemic lupus erythematosus: a critical review,” Journal of Autoimmunity, vol. 48, pp. 10–13, 2014.

P. Periasamy and V. L. Byrd, “Generative adversarial networks for lupus diagnostics,” in Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019, pp. 1–8.

Z. Wu, S. Zhao, Y. Peng, X. He, X. Zhao, K. Huang, X. Wu, W. Fan, F. Li, M. Chen et al., “Studies on different cnn algorithms for face skin disease classification based on clinical images,” IEEE Access, vol. 7, pp. 66 505–66 511, 2019.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition. Ieee, 2009, pp. 248–255.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in International Conference on Machine Learning, 2010.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in AAAI Conference on Artificial Intelligence, 2017.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in IEEE Conference on Computer Vision and Pattern recognition, 2017, pp. 4700–4708.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, 2015.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251–1258.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. A. Mobilenets, “Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in IEEE Conference on Computer Vision and Pattern recognition, 2018, pp. 8697–8710.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in IEEE conference on Computer Vision and Pattern Recognition, 2014, pp. 1725–1732.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.

C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1915–1929, 2012.

X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, and C. Zheng, “A weakly-supervised framework for covid-19 classification and lesion localization from chest ct,” IEEE Transactions on Medical Imaging, 2020.

S. Hu, Y. Gao, Z. Niu, Y. Jiang, L. Li, X. Xiao, M. Wang, E. F. Fang, W. Menpes-Smith, J. Xia et al., “Weakly supervised deep learning for covid-19 infection detection and classification from ct images,” IEEE Access, 2020.

A. E. Jatobá, L. L. Lima, and M. C. Oliveira, “Pulmonary nodule classification with 3d convolutional neural networks,” in Anais do XV Workshop de Visão Computacional. SBC, 2019, pp. 67–72.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.

kG Utsch, C. dos Santos, and J. Samatelo, “Convolutional neural network for skin lesion classification,” in Workshop de Vis˜ao Computacional. SBC, 2018, pp. 105–110.

S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani, R. Santangelo, M. Filippi, A. D. N. Initiative et al., “Automated classification of alzheimer’s disease and mild cognitive impairment using a single mri and deep neural networks,” NeuroImage: Clinical, vol. 21, p. 101645, 2019.

R. Ribani and M. Marengoni, “A survey of transfer learning for convolutional neural networks,” in Conference on Graphics, Patterns and Images - Tutorials (SIBGRAPI-T). IEEE, 2019, pp. 47–57.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. S´anchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.

E. M. Tan, A. S. Cohen, J. F. Fries, A. T. Masi, D. J. Mcshane, N. F. Rothfield, J. G. Schaller, N. Talal, and R. J. Winchester, “The 1982 revised criteria for the classification of systemic lupus erythematosus,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, vol. 25, no. 11, pp. 1271–1277, 1982.

M. C. Hochberg, “Updating the american college of rheumatology revised criteria for the classification of systemic lupus erythematosus,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, vol. 40, no. 9, pp. 1725–1725, 1997.

S. Gomathi and V. Narayani, “A proposed framework using cac algorithm to predict systemic lupus erythematosus (sle),” in World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), 2016, pp. 1–6.

S. Balderas-Díaz, K. Benghazi, G. Prados, and E. Miró, “Designing configurable and adaptive systems in ehealth,” in Workshop on ICTs for Improving Patients Rehabilitation Research Techniques. New York, NY, USA: Association for Computing Machinery, 2015, p. 118–121.

T. A. Rimi, N. Sultana, and M. F. A. Foysal, “Derm-nn: Skin diseases detection using convolutional neural network,” in International Conference on Intelligent Computing and Control Systems. IEEE, 2020, pp. 1205–1209.

J. Velasco, C. Pascion, J. W. Alberio, J. Apuang, J. S. Cruz, M. A. Gomez, B. Molina Jr, L. Tuala, A. Thio-ac, and R. Jorda Jr, “A smartphone-based skin disease classification using mobilenet cnn,” arXiv preprint arXiv:1911.07929, 2019.

S. Z. Y. Wasef, “Gender differences in systemic lupus erythematosus,” Gender Medicine, vol. 1, no. 1, pp. 12–17, 2004.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, p. 60, 2019.
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SOUZA, Jhonatan; DE OLIVEIRA, Tiago; CASA, Claudemir; ORTONCELLI, André. Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 36-40. DOI: