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Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques

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Języki publikacji
EN
Abstrakty
EN
In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
Rocznik
Strony
597--602
Opis fizyczny
Bibliogr. 22 poz., rys., wykr., tab.
Twórcy
autor
  • Faculty of Engineering/Benha University, Qalyubia, Egypt
  • Faculty of Engineering/Benha University, Qalyubia, Egypt
autor
  • Faculty of Engineering/Benha University, Qalyubia, Egypt
Bibliografia
  • [1] A.F. Jerant, J.T. Johnson, C.D. Sheridan, and T.J. Caffrey, “Early Detection and Treatment of Skin Cancer,” Am. Fam. Physician, 62 (2): 357–68, 375–6, 381–2, 2000.
  • [2] “World Health Organization,” Available: https://www.who.int/en/, [Accessed: 2018-09-10].
  • [3] M. Binder, M. Schwarz, A. Winkler, A. Steiner, A. Kaider, K. Wolff, and H. Pehamberger, “Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists,” Arch. Dermatol, 131(3):286-91, 1995.
  • [4] F. Nachbar, W. Stolz, T. Merkle, A. Cognetta, T. Vogt, and M. Landthaler, “The abcd rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions,” Journal of the American Academy of Dermatology, 30(4):551-9, 1994.
  • [5] G. Argenziano, G. Fabbrocini, P. Carli, V. De Giorgi, E. Sammarco, and M. Delfino, “Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comp. of the abcd rule of dermatoscopy and a new 7-point checklist based on pattern analysis,” Archives of Dermatology, 134(12):1563-70, 1998.
  • [6] P. Carli, E. Quercioli, S. Sestini, et al, “ Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology,” Br. J. Dermatol, 148(5), 981–984, 2003.
  • [7] A. Esteva, B. Kuprel, R. Novoa, et al, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, 542, 115–118, 2017.
  • [8] M. Walter, “Is this the end? machine learning and 2 other threats to radiologys future,” goo.gIIM9X3SF, 2016, Available: https://www.radiologybusiness.com/topics/technology-management/end-machine-learning-and-2-other-threats-radiologys-future, [Accessed: 2018-09-08].
  • [9] A. Nylund, “To be, or not to be Melanoma: Convolutional neural networks in skin lesion classification,” In: Dissertation (2016). Available: http://kth.diva-portal.org/smash/get/diva2:950147/FULLTEXT01.pdf. [Accessed: 2018-08-12].
  • [10] P. Mirunaliniy, A. Chandrabose, V. Gokul y, S. M. Jaisakthi.” Deep Learning for Skin Lesion Classification”, arXiv preprint arXiv: 1703.04364, 2017, Available: https://arxiv.org/pdf/1812.02316.pdf, [Accessed: 2018-09-15].
  • [11] J. Kawahara, A. BenTaieb, and G. Hamarneh, "Deep features to classify skin lesions," published in Conference: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 2016. [DOI: 10.1109/ISBI.2016.7493528].
  • [12] H. Haenssle, C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, and A. Blum, “Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists,” Ann Oncol: 29(8):1836-1842, 2018. [DOI: 10.1093/annonc/mdy166] [Medline: 29846502].
  • [13] P. Ridell, H. Spett, P. Herman, and Ö. Ekeberg, “Training Set Size for Skin Cancer Classification Using Google’s Inception v3,” 2017, Available,http://www.diva-portal.org/smash/get/diva2:1112097/FULLTEXT01.pdf, [Accessed: 2018-11-12].
  • [14] N. Codella, Q.-B. Nguyen, S. Pankanti, D. Gutman, B. Helba, A. Halpern, and J. R. Smith, "Deep learning ensembles for melanoma recognition in dermoscopy images," arXiv preprint arXiv: 1610. 04662, 2016.
  • [15] L. Deng, and D. Yu, “Deep Learning: Methods and Applications,” Foundations and Trends in Signal Processing, 7, 3–4, 197–387, 2014.
  • [16] M. Nielsen, “Neural Networks and Deep Learning,” Determination Press, 2015, Available: http://neuralnetworksanddeeplearning.com/, [Accessed: 2018-11-10].
  • [17] S. Albelwi and A. Mahmood, “A framework for designing the architectures of deep convolutional neural networks,” Entropy, 19(6), 242, 2017.
  • [18] S. Hijazi, R. Kumar, and C. Rowen, “Using Convolutional Neural Networks for Image Recognition,” Cadence Design Systems Inc., 2015.
  • [19] “International Skin Imaging Collaboration: Melanoma Project,” Available: https://www.isic-archive.com, [Accessed: 2018-09-17].
  • [20] K. Ramlakhan, and Y. Shang, “A Mobile Automated Skin Lesion Classification System,” Published in Conference: 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA, 2011. [DOI: 10.1109/ICTAI.2011.29].
  • [21] A. Menegola, M. Fornaciali, R. Pires, F. Bittencourt, s Avila, and E Valle, “Knowledge Transfer for Melanoma Screening with Deep Learning,” Published in Conference: 2017 IEEE 14th International Symposium on Biomedical Imaging, Melbourne, VIC, Australia,2017. [DOI: 10.1109/ISBI.2017.7950523]
  • [22] J. Burdick, O. Marques, J. Weinthal, and B. Furht, “Rethinking Skin Lesion Segmentation in a Convolutional Classifier,” Journal of Digit Imaging, 31(4), 435–440, 2018. [doi: 10.1007/s10278-017-0026-y].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d76a44e9-dc90-47a7-af9e-9321ef1bfeca
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