Abstract
Deep CNN techniques have dramatically become the state of the art in image classification. However, applying high-capacity Deep CNN in medical image analysis has been impeded because of scarcity of labeled data. This study has two primary contributions: first, we propose a classification model to improve performance of classification of skin lesion using Deep CNN and Data Augmentation. Second, we demonstrate the use of image data augmentation for overcoming the problem of data limitation and examine the influence of different number of augmented samples on the performance of different classifiers. The proposed classification system is evaluated using the largest public skin lesion testing dataset, containing 600 testing images, and 6,162 training images. New state-of-the-art performance result is archived with AUC (89.2% vs. 87.4%), AP (73.9% vs. 71.5%), and ACC (89.0% vs. 87.2%). In additional, we explore the influence of each image augmentation on the three classifiers and observe that performance of each classifier is influenced differently by each augmentation and has better results comparing with traditional methods. Thus, it is suggested that the performance of skin cancer classification and medial image classification could be improved further by applying data augmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Cancer Society: Cancer facts and figures 2016. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2016/cancer-facts-and-figures-2016.pdf. Accessed 15 Oct 2017
The International Skin Imaging Collaboration (ISIC). https://isic-archive.com/. Accessed 15 Oct 2017
PH2 Dataset. https://www.fc.up.pt/addi/ph2%20database.html. Accessed 15 Oct 2017
Large Scale Visual Recognition Challenge 2014 (ILSVRC 2014). http://image-net.org/challenges/LSVRC/2014/. Accessed 15 Oct 2017
ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection. http://challenge2017.isic-archive.com. Accessed 15 Oct 2017
Barata, C., Celebi, M.E., Marques, J.S.: Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health Inform. 19, 1146–1152 (2014)
Codella, N.C.F., Cai, J., Abedini, M., Garnavi, R., Halpern, A., Smith, J.R.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 118–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24888-2_15
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., Halpern, A.: Skin lesion analysis toward melanoma detection. In: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC). ArXiv e-prints arXiv:1710.05006 [cs.CV] (2017)
Codella, N.C.F., Nguyen, Q.B., Pankanti, S., Gutman, D., Helba, B., Halpern, A., Smith, J.R.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4), 5 (2017)
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
Ercal, F., Chawla, A., Stoecker, W.V., Lee, H.C., Moss, R.H.: Neural network diagnosis of malignant melanoma from color images. IEEE Trans. Biomed. Eng. 41, 837–845 (1994)
González-DÃaz, I.: Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. ArXiv e-prints: arXiv:1703.01976 [cs.CV] (2017)
Gutman, D., Codella, N.C.F., Celebi, E., Helba, B., Marchetti, M., Mishra, N., Halpern, A.: Skin lesion analysis toward melanoma detection. In: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, Hosted by the International Skin Imaging Collaboration (ISIC) (2016). ArXiv e-prints: arXiv:1605.01397 [cs.CV]
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 25, pp. 1097–1105 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Matsunaga, K., Hamada, A., Minagawa, A., Koga, H.: Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. ArXiv e-prints arXiv:1703.03108 [cs.CV] (2017)
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., Valle., E.: Knowledge transfer for melanoma screening with deep learning. ArXiv e-prints arXiv:1703.07479 [cs.CV] (2017)
Menegola, A., Tavares, J., Fornaciali, M., Li, L.T., Avila, S., Valle, E.: RECOD titans at ISIC challenge 2017. ArXiv e-prints arXiv:1703.04819 [cs.CV] (2017)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Artificial Intelligence, pp. 4278–4284 (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Computer Vision and Pattern Recognition (CVPR), vol. 2016, pp. 2818–2826 (2016)
Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: when to warp? In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–6 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Pham, TC., Luong, CM., Visani, M., Hoang, VD. (2018). Deep CNN and Data Augmentation for Skin Lesion Classification. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-75420-8_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75419-2
Online ISBN: 978-3-319-75420-8
eBook Packages: Computer ScienceComputer Science (R0)