Computer-Aided Diagnosis of Melanoma Skin Cancer: A Review

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 38)

Abstract

Skin cancer has a major impact on society in India and across the world. According to the figures given by the National Cancer Institute and SEER, estimated new cases of Melanoma in 2017 are 87,110. This figure is approximated 5.2% of all new cancer in 2017. As per the data obtained from the WORLD HEALTH RANKINGS, the death rate per 1,00000 is highest in New Zealand with 7.68% then Australia with 6.52%. It has been proved from the study that melanoma skin cancer is almost curable if it is diagnosed early and treated correctly; otherwise, it can spread to other parts of the body and become incurable. This paper presents the comparative study of various phases of computer-aided melanoma skin cancer detection system with the aim of providing the development achieved in the melanoma skin cancer detection by the research community from earlier period to the current time. This method starts from the image acquisition step followed by image preprocessing, segmentation, feature extraction, feature selection and classification steps. The input to this system is an image of affected skin area, and output labels this input image benign or malignant melanoma.

Keywords

Melanoma Preprocessing Segmentation Classification Benign Malignant Oncology Epiluminescence microscopy Neural network Fuzzy C-means 

References

  1. 1.
    Lee H, Chen YPP (2015) Image based computer aided diagnosis system for cancer detection. Expert Syst Appl 42:5356–5365CrossRefGoogle Scholar
  2. 2.
    Mehta P, Shah B (2016) Review on techniques and steps of computer aided skin cancer diagnosis. In: International conference on computational modeling and security, CMS 2016CrossRefGoogle Scholar
  3. 3.
    Cancer scenario in India: as per the statistics. Posted on 2/11/2014 by Daily excelsiorGoogle Scholar
  4. 4.
    Geller AC, Swetter SM, Brooks K, Demierre M, Yaroch AL (2007) Screening, early detection, and trends for melanoma: current status (2000–2006) and future directions. J Am Acad Dermatol 57:555–572CrossRefGoogle Scholar
  5. 5.
    Maglogiannis I, Doukas CN (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 13(5):721–733CrossRefGoogle Scholar
  6. 6.
    Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis. IEEE Tran Inf Technol Biomed 16(6):1239–1252CrossRefGoogle Scholar
  7. 7.
    Vestergaard ME, Menzies SW (2008) Automated diagnostic instruments for cutaneous melanoma, pp 1085–5629.  https://doi.org/10.1016/j.sder.2008.01.001
  8. 8.
    Rigel DS, Russak J, Friedman R (2010) The evolution of melanoma diagnosis: 25 years beyond the ABCDs. Wiley Online Library 60(5):301–316Google Scholar
  9. 9.
    Nakariyakul S, Casasent DP (2008) Improved forward floating selection algorithm for feature subset selection. In: International conference on wavelet analysis and pattern recognition, 30–31 Aug 2008, vol 2, pp 793–798Google Scholar
  10. 10.
    Madooei A, Drew MS (2013) A colour palette for automatic detection of blue-white veil. IEEE 2013Google Scholar
  11. 11.
    Celebi ME, Aslandogan YA (2004) Content-based image retrieval incorporating models of human perception. In: Proceedings of international conference on information technology: coding and computing (ITCC), vol 2, IEEE Computer Society Press, Los Alamitos, CA, p 2415Google Scholar
  12. 12.
    Zhou H, Chen M, Zou L, Gass R, Ferris L, Drogowski L, Rehg JM (2008) Spatially constrained segmentation of dermoscopy images. In: 5th IEEE international symposium on biomedical imaging, pp 800–803Google Scholar
  13. 13.
    Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17:491–502Google Scholar
  14. 14.
    Ruiz D, Berenguer V, Soriano A, SáNchez B (2011) A decision support system for the diagnosis of melanoma: a comparative approach. Expert Syst Appl 38(12):15217–15223CrossRefGoogle Scholar
  15. 15.
    Sumithra R, Suhil M, Guru DS (2015) Segmentation and classification of skin lesions for disease diagnosis. In: International conference on advanced computing technologies and applications (ICACTA-2015). Procedia Comput Sci 45:76–85CrossRefGoogle Scholar
  16. 16.
    Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6):362–373CrossRefGoogle Scholar
  17. 17.
    Sasikala M, Kumaravel N (2005) Comparison of feature selection techniques for detection of malignant tumor in brain images. In: IEEE Indicon 2005 conference, Chennai, India, 11–1 3 Dec 2005Google Scholar
  18. 18.
    Nakariyakul S, Casasent DP (2008) Improved forward floating selection algorithm for feature subset selection. In: Proceedings of the 2008 international conference on wavelet analysis and pattern recognition, Hong Kong, 30–31 Aug 2008Google Scholar
  19. 19.
    Jaleel JA, Salim S, Aswin RB (2013) Computer aided detection of skin cancer. In: International conference on circuits, power and computing technologies (ICCPCT), IEEE 2013Google Scholar
  20. 20.
    Lee T, Ng V, Gallagher R, Coldman A, McLean D (1997) DullRazor: a software approach to hair removal from images. Comput Biol Med 27(6):533–543CrossRefGoogle Scholar
  21. 21.
    Korotkov K, Garcia R (2012) Computerized analysis of pigmented skin lesions: a review. Artif Intell Med 56(2):69–90CrossRefGoogle Scholar
  22. 22.
    Kiani K, Sharafat AR (2011) E-shaver: an improved DullRazor® for digitally removing dark and light-colored hairs in dermoscopic images. Comput Biol Med 41(3):139–145CrossRefGoogle Scholar
  23. 23.
    Hoshyar AN, Al-Jumaily A, Hoshyar AN (2014) Comparing the performance of various filters on skin cancer images. In: International conference on robot PRIDE 2013–2014, Published by ElsevierCrossRefGoogle Scholar
  24. 24.
    Quintana J, Garcia R, Neumann L (2011) A novel method for color correction in epiluminescence microscopy. Comput Med Imaging Graph 35(7–8):646–652CrossRefGoogle Scholar
  25. 25.
    Wighton P, Lee TK, Lui H, McLean D, Atkins MS (2011) Chromatic aberration correction: an enhancement to the calibration of low-cost digital dermoscopes. Skin Res Technol 17(3):339–347CrossRefGoogle Scholar
  26. 26.
    Maglogiannis I, Zafiropoulos E, Kyranoudis C (2006) Intelligent segmentation and classification of pigmented skin lesions in dermatological images. https://doi.org/10.1007/11752912_23. In: DBLP conference: advances in artificial intelligence, proceedings of 4th Helenic conference on AI, SETN 2006, Heraklion, Crete, Greece, 18–20 May 2006CrossRefGoogle Scholar
  27. 27.
    Ebrahimi SM, Pourghassem H, Ashourian M (2010) Lesion detection in dermoscopy images using Sarsa reinforcement algorithm. In: 17th Iranian conference IEEE biomedical engineering (ICBME)Google Scholar
  28. 28.
    Ali AR, Couceiro MS, Hassenian AE (2014) Melanoma detection using fuzzy C-means clustering coupled with mathematical morphology. In: 14th international conference IEEE hybrid intelligent systems (HIS)Google Scholar
  29. 29.
    Sookpotharom S (2009) Border detection of skin lesion images based on fuzzy C-means thresholding. In: 3rd international conference IEEE genetic and evolutionary computing. WGEC’09Google Scholar
  30. 30.
  31. 31.
    Delgado D, Butakoff C, Ersboll BK, Stoecker WV (2008) Independent histogram pursuit for segmentation of skin lesions. IEEE Trans Biomed Eng 55:157–161CrossRefGoogle Scholar
  32. 32.
    Zhou H, Chen M, Zou L, Gass R, Ferris L, Drogowski L, Rehg JM (2008) Spatially constrained segmentation of dermoscopy images. In: 5th IEEE international symposium on biomedical imaging, pp 800–803Google Scholar
  33. 33.
    Chapter 3. Review of image segmentation methods. Available at http://shodhganga.inflibnet.ac.in/bitstream/10603/9107/8/08_chapter3.pdf
  34. 34.
  35. 35.
    Muthukrishnan R, Radha M (2011) Edge detection techniques for image segmentation. Int J Compu Sci Inf Technol 3(6)CrossRefGoogle Scholar
  36. 36.
    Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis. IEEE Trans Inf Technol Biomed 16(6):1239–1252CrossRefGoogle Scholar
  37. 37.
    Ikuma Y, Iyatomi H (2013) Production of the grounds for melanoma classification using adaptive fuzzy inference neural network. In: IEEE international conference systems, man, and cybernetics (SMC)Google Scholar
  38. 38.
    Ganster H, Pinz P, Rohrer R, Wildling E, Binder M, Kittler H (2001) Automated melanoma recognition. IEEE Trans Med Imaging 20(3)CrossRefGoogle Scholar
  39. 39.
    Maali Y, Al-Jumaily A (2012) Hierarchical parallel PSO-SVM Based subject-independent sleep apnea classification. ICONIP 2012, Part IV, LNCS 7666, pp 500–507CrossRefGoogle Scholar
  40. 40.
    Ramlakhan K, Shang Y (2011) A mobile automated skin lesion classification system. In: Proceedings of the 23rd IEEE international conference on tools with artificial intelligence (ICTAI’11), pp 138–141Google Scholar
  41. 41.
    Mabrouk MS, Sheha MA, Sharawy A (2012) Automatic detection of melanoma skin cancer using texture analysis. Int J Comput Appl 42(20):22–26Google Scholar
  42. 42.
    Rosado L, João M, Vasconcelos M, Ferreira M (2015) Pigmented skin lesion computerized analysis via mobile devices. SCCG 2015, Smolenice, Slovakia, ACM, 22–24 April 2015. http://dx.doi.org/10.1145/2788539.2788553
  43. 43.
    Correa DN, Paniagua LR, Noguera JL, Pinto-Roa DP, Toledo LA (2015) Computerized diagnosis of melanocytic lesions based on the ABCD method. In: Computing conference (CLEI), 2015 Latin American, 19–23 Oct 2015, pp 1–12Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Puneet Kumar Goyal
    • 1
  • Nirvikar
    • 2
  • Mradul Kumar Jain
    • 1
  1. 1.Uttarakhand Technical UniversityDehradunIndia
  2. 2.COER RoorkeeRoorkeeIndia

Personalised recommendations