Abstract
Industrial pollution resulting in ozone layer depletion has influenced increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer, melanoma, and other keratinocyte cancers. The incidence of deaths from melanoma has risen worldwide in the past two decades. Deep learning has been employed successfully for dermatologic diagnosis. In this work, we present a deep learning-based scheme to automatically segment skin lesions and detect melanoma from dermoscopy images. U-Net was used for segmenting out the lesion from surrounding skin. The limitation of utilizing deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial dropout to solve the problem of overfitting, and different augmentation effects were applied to the training images to increase data samples. The model was evaluated on two different datasets. It achieved a mean dice score of 0.87 and a mean Jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH2 dataset where it achieved a mean dice score of 0.93 and a mean Jaccard index of 0.87 with transfer learning. For classification of malignant melanoma, a DCNN-SVM model was used where we compared state-of-the-art deep nets as feature extractors to find the applicability of transfer learning in dermatologic diagnosis domain. Our best model achieved a mean accuracy of 92% on PH2 dataset. The findings of this study are expected to be useful in cancer diagnosis research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pham TC, Luong CM, Visani M, Hoang VD (2018) Deep CNN and data augmentation for skin lesion classification. Lecture Notes in Computer Science, pp 573–582. https://doi.org/10.1007/978-3-319-75420-8_54
Salido JAA, De La Salle University, Philippines, Ruiz C Jr (2018) Using deep learning for melanoma detection in dermoscopy images. Int J Mach Learn Comput 8:61–68. https://doi.org/10.18178/ijmlc.2018.8.1.664
Kohli JS, Tolomio E, Frigerio S, Maurichi A, Rodolfo M, Bennett DC (2017) Common delayed senescence of melanocytes from multiple primary melanoma patients. J Invest Dermatol 137:766–768. https://doi.org/10.1016/j.jid.2016.10.026
Ahmed HM, Al-azawi RJ, Abdulhameed AA (2018) Evaluation methodology between globalization and localization features approaches for skin cancer lesions classification. J Phys: Conf Ser 1003:012029. https://doi.org/10.1088/1742-6596/1003/1/012029
Glazer AM, Winkelmann RR, Farberg AS, Rigel DS (2016) Analysis of trends in US melanoma incidence and mortality. JAMA Dermatol. https://doi.org/10.1001/jamadermatol.2016.4512
Thao LT, Quang NH (2017) Automatic skin lesion analysis towards melanoma detection. In: 2017 21st Asia Pacific symposium on intelligent and evolutionary systems (IES). https://doi.org/10.1109/iesys.2017.8233570
Venkatesh GM, Naresh YG, Little S, O’Connor NE (2018) A deep residual architecture for skin lesion segmentation. Lecture Notes in Computer Science, pp 277–284. https://doi.org/10.1007/978-3-030-01201-4_30
Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 321:321–331. https://doi.org/10.1016/j.neucom.2018.09.013
D Bloice, M., Bloice MD, Stocker C, Holzinger A (2017) Augmentor: an image augmentation library for machine learning. J Open Source Softw 2:432. https://doi.org/10.21105/joss.00432
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
Chollet F (2015) Keras. https://github.com/fchollet/keras
Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3. https://doi.org/10.1186/s40537-016-0043-6
Wiatowski T, Bolcskei H (2018) A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Trans Inf Theor 64:1845–1866. https://doi.org/10.1109/TIT.2017.2776228
Mallat S (2012) Group invariant scattering. Commun Pure Appl Math 65:1331–1398. https://doi.org/10.1002/cpa.21413
Garcia-Gasulla D, Parés F, Vilalta A, Moreno J, Ayguadé E, Labarta J, Cortés U, Suzumura T (2018) On the behavior of convolutional nets for feature extraction. J Artif Intell Res 61:563–592. https://doi.org/10.1613/jair.5756
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature 546:686. https://doi.org/10.1038/nature21056
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2017.243
ISIC (2018) Skin lesion analysis towards melanoma detection
Codella NC, Gutman D, Celebi MEEA (2017) 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). arXiv:1710.05006, https://doi.org/10.1109/isbi.2018.8363547
Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imag. https://doi.org/10.1109/tmi.2018.2845918
Mendonca T, Ferreira PM, Marques JS, Marcal ARS, Rozeira J (2013) PH2 – a dermoscopic image database for research and benchmarking. In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 5437–5440. https://doi.org/10.1109/embc.2013.6610779
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nazi, Z.A., Abir, T.A. (2020). Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach with U-Net and DCNN-SVM. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_32
Download citation
DOI: https://doi.org/10.1007/978-981-13-7564-4_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7563-7
Online ISBN: 978-981-13-7564-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)