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A Survey work on Early Detection methods of Melanoma Skin Cancer


Affiliations
1 Dept. of ECE, JNTUA, Ananthapuramu, AP, India
2 Santhiram Engineering College, Nandyal, AP, India
     

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Melanoma is the most dangerous form of skin cancer and is responsible for more than 70 percent of skin cancer deaths. Melanomas develop from malignant melanocytes. Based on the years lost to cancer, melanoma would merit a higher ranking because relatively young people are affected by this malignancy. Melanoma is usually diagnosed in patients of a relatively young age; overall, the total number of patients suffering from melanoma is accumulating. Consequently, the total burden of melanoma is assumed to be increasing among Caucasian populations. As the overall burden of melanoma is increasing; prognosis strongly depends on the stage at diagnosis; and, most importantly, effective treatments for advanced stages are lacking, there is a high potential benefit for the prevention of melanoma. However, most of the established risk factors for melanoma, such as fair skin type, freckles, light eye color, older age, history of sun burns, clinical atypical nevi, prior melanoma, and family history of melanoma, are not amenable to intervention. Only sun burns and sun exposure are, at least in theory, amenable. Indeed, sun protection measures are part of melanoma prevention programs. In some high risk countries comprehensive sun protection programs have been implemented over a decade ago and sun screen use is widely promoted to the general public. These public health campaigns have increased awareness on skin cancer and the adverse events of excessive sun exposure, but failed to change the sun exposure behavior in the general population. Various researchers have shown their interest in early detection of melanoma and immense amount of work has been provided for the diagnosis of melanoma. In this paper the various methods in the process of early detection were discussed and the merits and demerits of the corresponding methods were present.

Keywords

Melanoma, Skin Cancer, Early Detection, Dermoscopy, Skin Lesion, Survey.
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  • C. Lu, M. Mahmood, N. Jha and M. Mandal, "Automated Segmentation of the Melanocytes in Skin Histopathological Images," in IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 2, pp. 284-296, March 2013.

  • J. Glaister, A. Wong and D. A. Clausi, "Segmentation of Skin Lesions from Digital Images Using Joint Statistical Texture Distinctiveness," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1220-1230, April 2014.

  • O. Abuzaghleh, B. D. Barkana and M. Faezipour, "Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1-12, 2015

  • F. E. S. Alencar, D. C. Lopes and F. M. Mendes Neto, "Development of a System Classification of Images Dermoscopic for Mobile Devices," in IEEE Latin America Transactions, vol. 14, no. 1, pp. 325-330, Jan. 2016.

  • E. M. A. Anas et al., "Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape $+$ Pose Model," in IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1789-1801, Aug. 2016.

  • R. Kasmi and K. Mokrani, "Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule," in IET Image Processing, vol. 10, no. 6, pp. 448-455, 6 2016.

  • Y. Yuan, M. Chao and Y. C. Lo, "Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance," in IEEE Transactions on Medical Imaging, vol. 36, no. 9, pp. 1876-1886, Sept. 2017

  • E. Ahn et al., "Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1685-1693, Nov. 2017.

  • L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham and D. Feng, "Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2065-2074, Sept. 2017.

  • L. Yu, H. Chen, Q. Dou, J. Qin and P. A. Heng, "Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks," in IEEE Transactions on Medical Imaging, vol. 36, no. 4, pp. 994-1004, April 2017.

  • N. C. F. Codella et al., "Deep learning ensembles for melanoma recognition in dermoscopy images," in IBM Journal of Research and Development, vol. 61, no. 4, pp. 5:1-5:15, July-Sept. 1 2017.

  • Julian St¨ottinger and et al., Skin Paths for Contextual Flagging Adult Videos, in Advances in Visual Computing, Springer Berlin Heidelberg, vol.5876, pp.303-314, 2009.

  • Mohammad Saber I., and Ali Y., Skin Color Segmentation in Fuzz YCBCR Color Space with the Mamdani Inference, in American Journal of Scientific Research, July (2011), pp.131-137, 2011.

  • F. A. Bahmer, P. Fritsch, J. Kreusch, H. Pehamberger, C. Rohrer, I. Schindera, et al., "Terminology in surface microscopy. Consensus meeting of the Committee on Analytical Morphology of the Arbeitsgemeinschaft Dermatologische For schung, Hamburg, Federal Republic of Germany, Nov. 17, 1989," J Am Acad Dermatol, vol. 23, pp. 1159-1162, Dec 1990.

  • A. F. Jerant, J. T. Johnson, C. Sheridan, T. J. Caffrey et al., “Early detection and treatment of skin cancer,” American family physician, vol. 62, no. 2, pp. 357–386, 2000.

  • R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2016,” CA: A cancer journal for clinicians, 2015.

  • C. M. Balch, A. C. Buzaid, S.-J. Soong, M. B. Atkins, N. Cascinelli, D. G. Coit, I. D. Fleming, J. E. Gershenwald, A. Houghton, J. M. Kirkwood et al., “Final version of the American joint committee on cancer staging system for cutaneous melanoma,” Journal of Clinical Oncology, vol. 19, no. 16, pp. 3635–3648, 2001.

  • R. Garnavi, M. Aldeen, M. E. Celebi, G. Varigos, and S. Finch, “Border detection in dermoscopy images using hybrid thresholding on optimized color channels,” Comput. Med. Imag. Graph., vol. 35, no. 2, pp. 105–115, 2011.

  • C. Li, C. Y. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1940–1949, Oct. 2008.

  • B. Erkol, R. H. Moss, R. Stanley, W. V. Stoecker, and E. Hvatum, “Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes,” Skin Res. Technol., vol. 11, no. 1, pp. 17–26, 2005.

  • J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 3431–3440.

  • D. S. Rigel, et al., "The incidence of malignant melanoma in the United States: issues as we approach the 21st century," Journal of the American Academy of Dermatology, vol. 34, pp. 839-847, 1996.

  • G. Argenziano, et al., "Dermoscopy, An Interactive Atlas," EDRA Medical Publishing, 2000.

  • M. Binder, et al., "Epiluminescence microscopy: A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists," Archives of Dermatology, vol. 131, pp. 286-291, 1995.

  • P. Wighton, et al., "A fully automatic random walker segmentation for skin lesions in a supervised setting," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009, ed: Springer, 2009, pp. 1108-1115.

  • American cancer society. Cancer facts & figures 2013; Available athttp://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf.

  • J Nalepa, T Grzejszczak, and M Kawulok, Wrist Localization in Color Images for Hand Gesture Recognition, in Advances in Intelligent Systems and Computing, Springer International Publishing, vol.242, pp.79-86, 2014.

  • Erdem, C.E., Ulukaya, S., Karaali, A. and Erdem, A.T., Combining Haar Feature and skin color based classifiers for face detection, in ICASSP 2011, pp.1497-1500, 2011.

  • [Chan C.S., Liu H., and Brown, D.J., Recognition of human motion from qualitative normalised templates, J. Intell. Robot. Syst., vol. 48(1), pp.79- 95, 2007.

  • Zui Z, Gunes, H., and Piccardi, M., Head detection for video surveillance based on categorical hair and skin colour models, in Image Processing (ICIP) 2009, pp.1137-1140, 2009.

  • H. Chen, et al., "DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation," in IEEE conference on Computer Vision and Pattern Recognition, 2016, pp. 2487-96.

  • X. Li, et al., "Contextual hypergraph modeling for salient object detection," in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3328-35.


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  • A Survey work on Early Detection methods of Melanoma Skin Cancer

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Authors

Tammineni Sreelatha
Dept. of ECE, JNTUA, Ananthapuramu, AP, India
M. V. Subramanyam
Santhiram Engineering College, Nandyal, AP, India
M. N. Giri Prasad
Dept. of ECE, JNTUA, Ananthapuramu, AP, India

Abstract


Melanoma is the most dangerous form of skin cancer and is responsible for more than 70 percent of skin cancer deaths. Melanomas develop from malignant melanocytes. Based on the years lost to cancer, melanoma would merit a higher ranking because relatively young people are affected by this malignancy. Melanoma is usually diagnosed in patients of a relatively young age; overall, the total number of patients suffering from melanoma is accumulating. Consequently, the total burden of melanoma is assumed to be increasing among Caucasian populations. As the overall burden of melanoma is increasing; prognosis strongly depends on the stage at diagnosis; and, most importantly, effective treatments for advanced stages are lacking, there is a high potential benefit for the prevention of melanoma. However, most of the established risk factors for melanoma, such as fair skin type, freckles, light eye color, older age, history of sun burns, clinical atypical nevi, prior melanoma, and family history of melanoma, are not amenable to intervention. Only sun burns and sun exposure are, at least in theory, amenable. Indeed, sun protection measures are part of melanoma prevention programs. In some high risk countries comprehensive sun protection programs have been implemented over a decade ago and sun screen use is widely promoted to the general public. These public health campaigns have increased awareness on skin cancer and the adverse events of excessive sun exposure, but failed to change the sun exposure behavior in the general population. Various researchers have shown their interest in early detection of melanoma and immense amount of work has been provided for the diagnosis of melanoma. In this paper the various methods in the process of early detection were discussed and the merits and demerits of the corresponding methods were present.

Keywords


Melanoma, Skin Cancer, Early Detection, Dermoscopy, Skin Lesion, Survey.

References