(45-5) 12 * << * >> * Russian * English * Content * All Issues

Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs
P.A. Lyakhov 1, U.A. Lyakhova 1

1 North-Caucasus Federal University, Stavropol, Russia

 PDF, 1640 kB

DOI: 10.18287/2412-6179-CO-863

Pages: 728-735.

Full text of article: Russian language.

Abstract:
The article proposes a neural network classification system for pigmented skin neoplasms with a preliminary processing stage to remove hair from the images. The main difference of the proposed system is the use of the stage of preliminary image processing to identify the location of the hair and their further removal. This stage allows you to prepare dermatoscopic images for further analysis in order to carry out automated classification and diagnosis of pigmented skin lesions. Modeling was carried out using the MatLAB R2020b software package on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system made it possible to increase the recognition accuracy of pigmented skin lesion images in 10 diagnostically important categories up to 80.81%. The use of the proposed system for the recognition and classification of images of dermatoscopic pigmented lesions by specialists will make it possible to increase the diagnostic efficiency in comparison with methods of visual diagnosis, and will also allow starting treatment at an earlier stage of the disease, which directly affects the survival and recovery rates for patients.

Keywords:
digital image processing, convolutional neural networks, dermatoscopic images, pigmented skin lesions, hair removal, melanoma.

Citation:
Lyakhov PA, Lyakhova UA. Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs. Computer Optics 2021; 45(5): 728-735. DOI: 10.18287/2412-6179-CO-863.

Acknowledgements:
This work was supported by the Government of the Russian Federation (state order no. 2.6035.2017/BCh), the Russian Foundation for Basic Research (project no. 18-07-00109 A), and by the Presidential Grant of the Russian Federation (project no. SP-2245.2018.5).

References:
  1. Fitzpatrick TB. Soleil et peau [In French].  J Med Esthet 1975; 2: 33-34.
  2. Fitzpatrick TB. The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 1988; 124(6): 869-871.
  3. Pathak MA, Jimbow K, Szabo G, Fitzpatrick TB. Sunlight and melanin pigmentation. In Book: Smith KC, ed. Photochemical and photobiological reviews. Vol 1. New York, London: Plenum Press; 1976: 211-239.
  4. Rogers HW. Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Arch Dermatol 2010; 146(3): 283-287.
  5. Stern RS. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 2010; 146(3): 279-282.
  6. Shlyakhtunov EA, Gidranovich AV, Lud NG, Lud LN, Kozhar VL, Prokos AV. Skin cancer: the current state of the problem [In Russian]. Bulletin of the Vitebsk State Medical University 2014; 13(3): 20-28.
  7. Bratchenko IA, Alonova MV, Myakinin OO, Moryatov AA, Kozlov SV, Zakharov VP. Hyperspectral visualization of skin pathologies in visible region. Computer Optics 2016; 40(2): 240-248. DOI: 10.18287/2412-6179-2016-40-2-240-248.
  8. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
  9. Alencar FES, Lopes DC, Neto FMM. Development of a system classification of images dermoscopic for mobile devices. IEEE Lat Am Trans 2016; 14(1): 325-330.
  10. Ramlakhan K, Shang Y. A mobile automated skin lesion classification system. IEEE 23rd Int Conf on Tools with Artificial Intelligence 2011: 138-141.
  11. Pai K, Giridharan A. Convolutional Neural Networks for classifying skin lesions. TENCON 2019: 1794-1796.
  12. Yang X, Li H, Wang L, Yeo SY, Su Y, Zeng Z. Skin lesion analysis by multi-target deep neural networks. 2018 40th Annual Int Conf of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018: 1263-1266.
  13. Zaqout IS. An efficient block-based algorithm for hair removal in dermoscopic images. Computer Optics 2017; 41(4): 521-527. DOI: 10.18287/2412-6179-2017-41-4-521-527.
  14. Lee T, Ng V, Gallagher R, Coldman A, McLean D. Dullrazor®: A software approach to hair removal from images. Comput Biol Med 1997; 27(6): 533-543.
  15. Schmid-Saugeona P, Guillodb J, Thirana JP. Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 2003; 27(1): 65-78.
  16. Lim JS. Two-dimensional signal and image processing. Englewood Cliffs, NJ: Prentice Hall; 1990.
  17. Fiorese M, Peserico E, Silletti A. VirtualShave: automated hair removal from digital dermatoscopic images. 2011 Annual Int Conf of the IEEE Engineering in Medicine and Biology Society 2011: 5145-5148.
  18. Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Pearson; 2007.
  19. Chervyakov NI, Lyakhov PA, Deryabin MA, Nagornov NN, Valueva MV, Valuev GV. Residue number system-based solution for reducing the hardware cost of a Convolutional Neural Network. Neurocomputing 2020; 407: 439-453.
  20. Zhang J, Shao K, Luo X. Small sample image recognition using improved Convolutional Neural Network. J Vis Commun Image Represent 2018; 55: 640-647.
  21. ISIC Melanoma Project. Source: <https://www.isic-archive.com/>.
  22. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset: a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 2018; 5: 180161.
  23. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. New Delhi: McGraw-Hill Education; 2010.
  24. Lyakhova UA, Lyakhov PA, Chervyakov NI, Kaplun DI, Voznesensky AS. Neural network method for determining skin lesions by images. 9th Mediterranean Conference on Embedded Computing (MECO) 2020: 1-4.
  25. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 25: 1097-1105.
  26. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. Source: <https://arxiv.org/abs/1602.07360>.
  27. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE conf on computer vision and pattern recognition 2016: 770-778.
  28. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118.
  29. Albahar MA. Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access 2019; 7: 38306-38313.
  30. Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol 2018; 138(7): 1529-1538.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20