A comparative study of convolutional neural networks for classification of pigmented skin lesions

• Natalia Camillo do Carmo UFV
• João Fernando Mari UFV

DOI: https://doi.org/10.5753/wvc.2021.18909

Resumo

Skin cancer is one of the most common types of cancer in Brazil and its incidence rate has increased in recent years. Melanoma cases are more aggressive compared to nonmelanoma skin cancer. Machine learning-based classification algorithms can help dermatologists to diagnose whether skin lesion is melanoma or non-melanoma cancer. We compared four convolutional neural networks architectures (ResNet-50, VGG16, Inception-v3, and DenseNet-121) using different training strategies and validation methods to classify seven classes of skin lesions. The experiments were executed using the HAM10000 dataset which contains 10,015 images of pigmented skin lesions. We considered the test accuracy to determine the best model for each strategy. DenseNet-121 was the best model when trained with fine-tuning and data augmentation, 90% (k-fold crossvalidation). Our results can help to improve the use of machine learning algorithms for classifying pigmented skin lesions.

Palavras-chave: Skin cancer, machine learning, convolutional neural networks, classification

Referências

INCA, 2020. [Online]. Available: https://www.inca.gov.br/tipos-de-cancer/cancer-de-pele-nao-melanoma

A. C. Society, 2020. [Online]. Available: https://www.cancer.org/cancer/melanoma-skin-cancer.html

M. A. Al-Masni, D.-H. Kim, and T.-S. Kim, “Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification,” Computer Methods and Programs in Biomedicine, vol. 190, p. 105351, 2020.

R. C. Maron, M. Weichenthal, J. S. Utikal, A. Hekler, C. Berking, A. Hauschild, A. H. Enk, S. Haferkamp, J. Klode, D. Schadendorf et al., “Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks,” European Journal of Cancer, vol. 119, pp. 57-65, 2019.

S. H. Kassani and P. H. Kassani, “A comparative study of deep learning architectures on melanoma detection,” Tissue and Cell, vol. 58, pp. 76- 83, 2019.

D. N. Le, H. X. Le, L. T. Ngo, and H. T. Ngo, “Transfer learning with class-weighted and focal loss function for automatic skin cancer classification,” arXiv preprint arXiv:2009.05977, 2020.

E. H. Mohamed and W. H. El-Behaidy, “Enhanced skin lesions classification using deep convolutional networks,” in 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). Cairo: IEEE, 2019, pp. 180-188.

S. S. Chaturvedi, J. V. Tembhurne, and T. Diwan, “A multi-class skin cancer classification using deep convolutional neural networks,” Multimedia Tools and Applications, pp. 1-22, 2020.

A. Rezvantalab, H. Safigholi, and S. Karimijeshni, “Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms,” arXiv preprint arXiv:1810.10348, 2018.

S. S. Chaturvedi, K. Gupta, and P. S. Prasad, “Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using mobilenet,” in International Conference on Advanced Machine Learning Technologies and Applications. India: Springer, 2020, pp. 165-176.

P. Tschandl, C. Rosendahl, and H. Kittler, “The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific data, vol. 5, no. 1, pp. 1-9, 2018.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Massachusetts: MIT Press, 2016, http://www.deeplearningbook.org.

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Computational intelligence and neuroscience, vol. 2018, 2018.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition. Beijing: IEEE, 2016, pp. 770-778.

H. Lei, T. Han, F. Zhou, Z. Yu, J. Qin, A. Elazab, and B. Lei, “A deeply supervised residual network for hep-2 cell classification via cross-modal transfer learning,” Pattern Recognition, vol. 79, pp. 290-302, 2018.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.

E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.