A Survey on Classification of Malignant Melanoma and Benign Skin Lesion by Using Machine Learning Techniques
DOI:
https://doi.org/10.61841/wdwt7k95Keywords:
-Skin cancer detection, dermoscopy, laptop aided diagnosis, category, melanoma detectionAbstract
Malignant cancer is one of the many skin most cancers types. Melanoma treatment relies upon the stage of detection. Several techniques which includes clinical and automatic are popular in prognosis of melanoma. Image-based digital diagnosis systems facilitate early cancer detection. The kingdom of the artwork in pc aided analysis device look at recent practices of the above cited systems the use of different processes which includes k-clustering, fuzzy good judgment etc. Research demanding situations related to the different processes concerned in computer aided prognosis of pores and skin cancer are also highlighted. Statistical traits of the dermoscopy picture may be used as efficient discriminating features for detection. Different parameters like texture capabilities, coloration space, form and asymmetry can in addition the expertise of upcoming researchers in the discipline of digital skin prognosis.
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1. I. Maglogiannis and C.N. Doukas, “Overview of advanced computer vision systems for skin lesions characterization”, IEEE Transactions on Information Technology in Biomedicine, 2009
2. S. Jain, V. Jagtap and N. Pise, “Computer aided melanoma skin cancer detection using image processing”, Procedia Computer Science, pp. 736–741, 2015.
3. Q. Abbas, M. E. Celebi and I. Fondón, “Hair removal methods: A comparative study for dermoscopy images”, Biomedical Signal Processing and Control, vol. 6 issue 6, pp. 395–404, 2011.
4. Q. Abbas, I. F. Garcia, M. Emre Celebi, and W. Ahmad, “A Feature-Preserving Hair Removal Algorithm for Dermoscopy Images”, Skin Research and Technology, vol. 19 issue 1,pp 27–36. 2013.
5. M. E. Celebi., Y. A. Aslandogan and P. R. Bergstresser,. “Unsupervised border detection of skin lesion images”, International Conference on Information Technology: Coding and Computing (ITCC’05) , vol. II,
pp. 1–6, 2005
6. I. Maglogiannis, S. M. and K. Delibasis , “Hair Removal on Dermoscopy Images”, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2960–2963
,2015
7. J. Korjakowska. and R. Tadeusiewicz, “Hair removal from dermoscopic color images”, Bio-Algorithms and Med-Systems, vol. 9 issue 2, pp.53–58. 2013
8. A. Masood and A. Al-jumaily, “Differential Evolution based Advised SVM for Histopathalogical Image Analysis for Skin Cancer Detection”, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society , pp. 781 –784, 2015
9. M E. Celebi, A. Hassan, Kingravi, Hitoshi Iyatomi, Y. Alp Aslandogan, W. V., Stoecker, Randy H. Moss, Joseph M. Malters, James M. Grichnik and Marghoob, Harold S. Rabinovitz, and S. W. M., “Border detection in dermoscopy images using statistical region merging”, Skin Research and Technology, 48(Suppl 2), 1–6. 2008
10. A. Sultana, I. Dumitrache, M. Vocurek and M. Ciuc, “Removal of artifacts from dermatoscopic images”, IEEE International Conference on Communications, 2014
11. M. E. Celebi, “Detection of blue-white veil areas in dermoscopy images using machine learning techniques”, Proceedings of SPIE, 2006
12. H. Ganster, A. Pinz, R. Rohrer, R., E. Wildling, E., M. Binder, M., and H. Kittler, “Automated melanoma recognition”, IEEE Transactions on Medical Imaging, 2001
13. I. Stanganelli and M. A. P, “Dermoscopy: Overview, Technical Procedures and Equipment, Color” , Available: http://emedicine.medscape.com/article/1130783-overview#a2, Retrieved June 2, 2016
14. F. Nachbar, W. Stolz, T. Merkle, A. B. Cognetta, T. Vogt, ,M. Landthaler. and G. Plewig, “The ABCD rule of dermatoscopy”, Journal of the American Academy of Dermatology, vol.30 issue 4, pp. 551–559, 1994
15. W. Stolz, A. Riemann, A. B. Cognetta, L. Pillet, W. Abmayr, D. Holzel, P. Bilek, F. Nachbar, M. Landthaler,and O. Braun-Falco, “ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma”, European Journal of Dermatology, vol. 4, pp. 521–527, 1994
16. W. Stolz, A. Reimann, and A. Cognetta, “ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma”, 1993
17. H. Tsao, J. M. Olazagasti, K. M. Cordoro, J. D. Brewer, S. C. Taylor, J. S. Bordeaux and W.S. Begolka, “Early detection of melanoma: reviewing the ABCDEs”, Journal of the American Academy of Dermatology, vol. 72 issue 4, pp.717–23, 2015.
18. G. Betta, G. Di Leo, G. Fabbrocini, A. Paolillo and M. Scalvenzi, (2005). Automated application of the “7-point checklist” diagnosis method for skin lesions: Estimation of chromatic and shape parameters”, IEEE Instrumentation and Measurement Technology Conference, pp. 1818–1822, 2005
19. T. Wadhawan, N. Situ, H. Rui, K. Lancaster, X. Yuan and G. Zouridakis, “Implementation of the 7-point checklist for melanoma detection on smart handheld devices”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3180–3183, 2011
20. F. M. Walter, A. T. Prevost, J. Vasconcelos, P. N. Hall, N. P. Burrows, H. C. Morris and J. D. Emery, “Using the 7-point checklist as a diagnostic aid for pigmented skin lesions in general practice: A diagnostic validation study”, British Journal of General Practice, vol. 63, pp. 345–353, 2013
21. A. R. S. Marcal, T. Mendonca, M. A. Pereira, J. Rozeira and C. S. P. Silva, “Evaluation of the Menzies method potential for automatic dermoscopic image analysis”, Proceedings of the International Symposium, CompIMAGE 2012, pp. 103–108, 2012
22. P. Rubegni, G. Cevenini, M. Burroni, R. Perotti, G. Dell’Eva, ,P. Sbano and L. Andreassi, “Automated diagnosis of pigmented skin lesions” , International Journal of Cancer, vol. 101 issue. 6, pp. 576–580, 2002
23. M. Lingala, R. Joe Stanley, R. K. Rader, J. Hagerty, , H. S. Rabinovitz, , M. Oliviero, and Stoecker, “Fuzzy logic color detection: Blue areas in melanoma dermoscopy images”, Computerized Medical Imaging and Graphics, vol. 38 issue 5, pp. 403–410, 2014
24. P. Cudek and Z. Hippe, “Melanocytic Skin Lesions : A New Approach to Color Assessment”, 2015 8th International Conference on Human System Interaction (HSI), pp. 99–101, 2015
25. A. Masood and A. Al-jumaily, “Differential Evolution based Advised SVM for Histopathalogical Image Analysis for Skin Cancer Detection”, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ,pp. 781 – 784, 2015
26. T. Wadhawan, N. Situ, H. Rui, X. Yuan, and G. Zouridakis,“SkinScan c : A PORTABLE LIBRARY FOR MELANOMA DETECTION ON HANDHELD DEVICES”. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 133–136, 2011
27. T. Wadhawan, N. Situ, H. Rui, K. Lancaster, X. Yuan, and G. Zouridakis, “Implementation of the 7-point checklist for melanoma detection on smart handheld devices”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS pp. 3180–3183, 2011
28. T. T. Do, Y. Zhou, H. Zheng, N. M. Cheung and D. Koh, “Early Melanoma Diagnosis with Mobile Imaging”, 36th IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 6752–6757, 2014
29. PH2 Database. (2013). PH2 Database. [Online] Avaiable http://www.fc.up.pt/addi/ph2 database.html Retrieved Retrieved August 19, 2016
30. DermIS.(2016).DermIS.[Online]Avaiable http://www.dermis.net/dermisroot/en/home/index.htm Retrieved June 29, 2016
31. DermQuest Image Library (2016). DermQuest Image Library [Online] Avaiable https://www.dermquest.com/image-library/ Retrieved June 29, 2016
32. ISBI 2016: Skin Lesion Analysis Towards Melanoma Detection [Online] Available at http://biomedicalimaging.org/2016/?page_id=422, Retrieved 14 November 14, 2016
33. R. B. Oliveira a, M. E.. Filho, Z Ma , J. P. Papa,A. S. Pereira ,J. M. R.S. Tavares, “Computational methods for the image segmentation of pigmented skin lesions: A review”, Computer methods and programs in biomedicine vol 131, 127-141, 2016
34. D. Grossman, AG. Goodson, “Strategies for early melanoma detection: approaches to the patient with nevi,” Pubmed. J., no. 60, pp. 719-738, 2009.
35. DS. Rigel, J. Russak, R. Friedman, “The evolution of melanoma diagnosis: 25 years beyond the ABCD,” CA: To Cancer J., no. 5, pp. 301316
36. MA. Tucker, “Melanoma epidemiology,” Hematology/Oncology Clinics of North America, no. 23, pp. 383-395, 2009.
37. C. Barata, J. S. Marques, and J. Rozeira, “A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters,” Biomedical Engineering, IEEE Transactions on, vol. 59, no. 10, pp. 2744-2754, 2012.
38. T. F. Chan and L. A. Vese, "Active contours without edges," Image processing, IEEE transactions on, vol. 10,
pp. 266-277, 2001.
39. N. Otsu, "A threshold selection method from graylevel histograms," Automatica, vol. 11, pp. 23-27, 1975.
40. N. Smaoui, and S. Bessassi, “A developed system for melanoma diagnosis,” International Journal of Computer Vision and Signal Processing, vol. 3, no. 1, pp. 10-17, 2013.
41. Omar Abuzaghleh, Buket D. Barkana and Miad Faezipour, “Automated Skin Lesion Analysis Based on Color and Shape Geometry Feature Set for Melanoma Early Detection and Prevention”, 978-1-4577-1343-9/12/$26.00 ©2014 IEEE.
42. Naser Alfed, Fouad Khelifi, “Pigment network-based skin cancer detection”, 978-1-4244-9270-1/15/$31.00
©2015 IEEE.
43. Hiam Alquran, Isam Abu Qasmieh, Ali Mohammad Alqudah, “The Melanoma Skin Cancer Detection and Classification using Support Vector Machine”, 978-1-5090-5969-0/17/$ 31.00 ©2017 IEEE.
44. Ms. Amulya P M, “A Study on Melanoma Skin Cancer Detection Techniques”, 978-1-5386-1959-9/17/$31.00 ©2017 IEEE.
45. Quadri, Kunle Alabi, Christian Eseigbe Imafidon, Rufus Ojo Akomolafe, and . 2019. Kolaviron mitigates proteinuria and potentiates loop diuresis in Wistar rats: Relevance to normal renal function. Journal of Complementary Medicine Research, 10 (1), 58-67. doi:10.5455/jcmr.20190112122816
46. Copland, I.B.Mesenchymal stromal cells for cardiovascular disease(2011) Journal of Cardiovascular Disease Research, 2 (1), pp. 3-13. DOI: 10.4103/0975-3583.78581
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