Abstract
The word cancer is enough to send many people into a spin. However, most types of skin cancer have a very favorable prognosis. They are common and very treatable. Melanoma is the skin cancer of most concern. Minor skin cancers often appear as a spot or sore that will not heal. Melanomas may arise in a preexisting skin mole that has become darker or changed in appearance. More often they will appear as a new mole or an unusual freckle. Nearly all skin cancers are related to excessive UV radiation. The depletion of the earth’s ozone layer also appears to be increasing the risk of developing skin cancer. With melanoma, family history also seems to be a factor. Detection at the melanoma in situ stage provides the highest curable rate for melanoma. The aim of this paper is to provide the summary of all the available methods and stages of melanoma identification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Weinstock, M.: Cutaneous melanoma: Public health approach to early detection. Dermatol. Ther. 19(1), 22–31 (2006)
Halpern, A., Marghoob, A., Bialoglow, T., Witmer, W., Slue, W.: Standardized positioning of patients (poses) for whole body cutaneous photography. J. Am. Acad. Dermatol. 49(4), 593–598 (2003)
Massone, C., Brunasso, A.M., Campbell, T.M., Soyer, H.P.: Mobile teledermoscopy—melanoma diagnosis by one click in Seminars in cutaneous medicine and surgery, pp. 203–205 (2009)
Wadhawan, T., Situ, N., Lancaster, K., Yuan, X., Zouridakis, G.: SkinScan©: A portable library for melanoma detection on handheld devices in Biomedical Imaging: From Nano to Macro. In: IEEE International Symposium, pp. 133–136 (2011)
Boldrick, J., Layton, C., Ngyuen, J., Swtter, S.: Evaluation of digital dermoscopy in a pigmented lesion clinic: Clinician versus computer assessment of malignancy risk. J. Am. Acad. Dermatol. 56(3), 417–421 (2007)
Doukas, C., Stagkopoulos, P., Kiranoudis, C., Maglogiannis, I.: Automated skin lesion assessment using mobile technologies and cloud platforms. In: Engineering in Medicine and Biology Society(EMBC), 2012 Annual International Conference of the IEEE, pp. 2444–2447 (2012)
Grana, C. Pellacani, G., Cucchiara, R., Seidenari, S.: A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Trans. Med. Imaging 22(8), 959–964 (2003)
Abbas, Q., Celebi, M.E., Garcia, I.F., Ahmad, W.: Melanoma recognition framework based on expert definition of abcd for dermoscopic images. Skin Res. Technol. 19(1), e93–e102 (2013)
Barata, C., Ruela, M., Francisco, M., Mendonca, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 99, 1–15 (2013)
Surowka, G., Grzesiak-Kopec, K.: Different learning paradigms for the classification of melanoid skin lesions using wavelets. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 3136–3139 Aug 2007
Marques, J.S., Barata, C., Mendonca, T.: On the role of texture and color in the classification of dermoscopy images. In: IEEE 34th EMBC, pp. 4402–4405 (2012)
Md. Abu Mahmoud, K., Al-Jumaily, A., Takruri, M.: Wavelet and curvelet analysis for automatic identification of melanoma based on neural network classification. Int. J. Comput. Inf. Syst. Ind. Manag. (IJCISIM). ISSN 2150–7988, 5, 606–614 (2013)
Ramlakhan, K., Shang, Y.: Mobile, automated skin lesion classification system in tools with artificial intelligence (ICTAI). In: 23rd IEEE International Conference, pp. 138–141 (2011)
Friedman, R.J., Rigel, D.S., Kopf, A.W.: Early detection of malignant melanoma: The role of physician examination and self-examination of the skin. CA: A Cancer J. Clin. 35(3) (May/June 1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Reshma, M., Priestly Shan, B. (2016). Review of Image Acquisition and Classification Methods on Early Detection of Skin Cancer. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_85
Download citation
DOI: https://doi.org/10.1007/978-981-10-0448-3_85
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0447-6
Online ISBN: 978-981-10-0448-3
eBook Packages: EngineeringEngineering (R0)