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Detection of Malignant Melanoma with Supervised Learning:A Review


Affiliations
1 RCEW, India
2 RCEW, CSE Department, India
     

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Malignant melanoma is increasing in some countries especially in Australia. Computational systems have been proposed for automatic diagnosis of skin cancer in order to aid dermatologist in early assessment of such disease. Several ways for image analysis targeted on dermoscopy imagery are projected for CAD system. To assist dermatologists in their diagnosis is the main aim of such system at an effective automated diagnosis. Malignant Melanoma needs to be diagnosed at their early stage, when the patient has a higher probability of cure.

Malignant melanoma is a kind of skin cancer whose severity even leads to death. For decreasing the chances of death earlier detection of Melanoma is necessary and the clinicians can treat the patients to increase the chances of survival. For detection of Melanoma using its features there are some machine learning algorithms are developed. This paper presents the review of Malignant Melanoma with supervised learning.


Keywords

Malignant Melanoma, Supervised Learning.
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  • Cancer facts and figures. 2013. [http://www.cancer.org/research/cancerfactsfigures/cancerfactsfigures/cancer-facts-figures-2013]

  • Jemal A, Bray F, Center M, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90. doi: 10.3322/caac.20107.

  • Braun R, Rabinovitz H, Oliviero M, Kopf A, Saurat J. Dermoscopy of pigmented lesions. J Am Acad Dermatol. 2005;52(1):109–121. doi: 10.1016/j.jaad.2001.11.001.

  • Stolz W, Riemann A, Cognetta A. ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma. Eur J Dermatol. 1994;4:521–527.

  • Argenziano G, Fabbrocini G, Carli P, Giorgi V, Sammarco E, Delfino M. Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch Derm. 1998;134:1563–1570.

  • Menzies SW, Ingvar C, McCarthy WH. A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma. Melanoma Res. 1996;6(1):55–62. doi: 10.1097/00008390-199602000-00008.

  • Geller AC, Swetter SM, Brooks K, Demierre M, Yaroch AL. Screening, early detection, and trends for melanoma: current status (2000–2006) and future directions. J Am Acad Dermatol. 2007;57:555–572. doi: 10.1016/j.jaad.2007.06.032.

  • Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Sera F, Binder M, Cerroni L, De Rosa G, Ferrara G, Hofmann-Wellenhof R, Landthaler M, Menzies SW, Pehamberger H, Piccolo D, Rabinovitz HS, Schiffner R, Staibano S, Stolz W, Bartenjev I, Blum A, Braun R, Cabo H, Carli P, De Giorgi V, Fleming MG, Grichnik JM, Grin CM, Halpern AC, Johr R. et al. Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J Am Acad Dermatol. 2003;48:679–693. doi: 10.1067/mjd.2003.281.

  • Manousaki AG, Manios AG, Tsompanaki EI, Panayiotides JG, Tsiftsis DD, Kostaki AK, Tosca AD. A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report. Int J Dermatol. 2006;45(4):402–410. doi: 10.1111/j.1365-4632.2006.02726.x.

  • Ganster H, Pinz A, Rohrer R, Wildling E, Binder M, Kittler H. Automated melanoma recognition. IEEE Trans Med Imaging. 2001;20(3):233–239. doi: 10.1109/42.918473.

  • Alcón JF, Ciuhu C, Kate W, Heinrich A, Uzunbajakava N, Krekels G, Siem D, de Haan G. Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J Select Top Sign Process. 2009;3(1):14–25.

  • Menzies SW, Bischof L, Talbot H, Gutenev A, Avramidis M, Wong L, Lo SK, Mackellar G, Skladnev V, McCarthy W, Kelly J, Cranney B, Lye P, Rabinovitz H, Oliviero M, Blum A, Varol A, De'Ambrosis B, McCleod R, Koga H, Grin C, Braun R, Johr R. The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch Dermatol. 2005;141:1388–1396.

  • Hoffmann K, Gambichler T, Rick A. Diagnostic and neural analysis of skin cancer (danaos). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy. Br J Dermatol. 2003;149:801–809. doi: 10.1046/j.1365-2133.2003.05547.x.

  • Jamora MJ, Wainwright BD, Meehan SA, Bystryn JC. Improved identification of potentially dangerous pigmented skin lesions by computerized image analysis. Arch Derm. 2003;139:195–198.

  • Monheit G, Cognetta AB, Ferris L, Rabinovitz H, Gross K, Martini M, Grichnik JM, Mihm M, Prieto VG, Googe P, King R, Toledano A, Kabelev N, Wojton M, Gutkowicz-Krusin D. The performance of MelaFind: a prospective multicenter study. Arch Dermatol. 2011;147(2):188–194. doi: 10.1001/archdermatol.2010.302.

  • Elbaum M, Kopf A, Rabinovitz H, Langley R, Kamino H. Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study. J Am Acad Dermatol. 2001;44:207–218. doi: 10.1067/mjd.2001.110395.

  • Pham T, Spott T, Svaasand L, Tromberg B. Quantifying the properties of two-layer turbid media with frequency-domain diffuse reflectance. Appl Opt. 2000;39:4733–4745. doi: 10.1364/AO.39.004733.

  • Melanoma skin cancer detection and classification based on supervised and unsupervised learning. H. R. Mhaske , et. al 2013 INSPEC Accession Number: 14047399 DOI: 10.1109/CCUBE.2013.6718539

  • [ S. Sujitha 2015] has used some computer aided image processing techniques for melanoma diagnosis. INSPEC Accession Number: 15510115 DOI: 10.1109/NCCCIS.2015.7295900

  • [Ammara Masood 2015 ] An active area of research is an Automated diagnosis of skin cancer with different classification methods proposed so far. INSPEC Accession Number: 15287069 DOI: 10.1109/NER.2015.7146798

  • [Maria João M. Vasconcelos ,et al ,2015 ]The incidence of melanoma has been increasing steadily over the past few decades throughout most of the world. INSPEC Accession Number: 15278137 DOI: 10.1109/MeMeA.2015.7145268


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  • Detection of Malignant Melanoma with Supervised Learning:A Review

Abstract Views: 336  |  PDF Views: 3

Authors

Neena Agrawal
RCEW, India
Vineet Khanna
RCEW, CSE Department, India

Abstract


Malignant melanoma is increasing in some countries especially in Australia. Computational systems have been proposed for automatic diagnosis of skin cancer in order to aid dermatologist in early assessment of such disease. Several ways for image analysis targeted on dermoscopy imagery are projected for CAD system. To assist dermatologists in their diagnosis is the main aim of such system at an effective automated diagnosis. Malignant Melanoma needs to be diagnosed at their early stage, when the patient has a higher probability of cure.

Malignant melanoma is a kind of skin cancer whose severity even leads to death. For decreasing the chances of death earlier detection of Melanoma is necessary and the clinicians can treat the patients to increase the chances of survival. For detection of Melanoma using its features there are some machine learning algorithms are developed. This paper presents the review of Malignant Melanoma with supervised learning.


Keywords


Malignant Melanoma, Supervised Learning.

References