Abstract
Early detection of skin cancer is very important as it is one of the dangerous form of cancer spreading vigorously among humans. With the advancement of mobile technology; mobile enabled skin cancer detection systems are really demanding but currently very few real time skin cancer detection systems are available for general public and mostly available are the paid. In this paper authors proposed a real time mobile enabled health care system for the detection of skin melanoma for general users. Proposed system is developed using computer vision and image processing techniques. Noise is removed by applying the Gaussian filter. For segmentation Grab Cut algorithm is used. Support Vector Machine (SVM) is applied as a classification technique on the texture features like area, perimeter, eccentricity etc. The sensitivity and specificity rate achieved by the m-Skin Doctor is 80% and 75% respectively. The average time consumed by the application for classifying one image is 14938 ms.
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
Similar content being viewed by others
References
Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)
Abuzaghleh, O., Barkana, B.D., Faezipour, M.: Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J. Transl. Eng. Health Med. 3, 1–12 (2015)
Melanoma Skin Cancer. http://www.skincancer.org/skin-cancer-information/melanoma. Accessed 4 Feb 2016
American cancer society. Cancer facts & figures 2015. Atlanta: American cancer society. Technical report, American Cancer Society (2015)
Nachbar, F., Stolz, W., Merkle, T., Cognetta, A.B., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., Plewig, G.: The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol. 30, 551–559 (1994)
Binu Sathiya, S., Kumar, S., Prabin, A.: A survey on recent computer-aided diagnosis of melanoma. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, pp. 1387–1392 (2014)
Argenziano, G., Fabbrocini, G., Carli, P., De 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. Dermatol. 134, 1563–1570 (1998)
Argenziano, G., Soyer, H.P., Chimenti, S., Talamini, R., Corona, R., Sera, F., Binder, M., Cerroni, L., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003). http://www.sciencedirect.com/science/article/pii/S0190962203003530
Peter Braun, R., Rabinovitz, H.S., Oliviero, M., Kopf, A.W., Saurat, J.-H.: Dermoscopy of pigmented skin lesions. J. Am. Acad. Dermatol. 52, 109–121 (2005)
Kassianos, A.P., Emery, J.D., Murchie, P., Walter, F.M.: Smartphone applications for melanoma detection by community patient and generalist clinician users: a review. Br. J.Dermatol., 1507–1518
Android phones are getting cheaper. http://uk.businessinsider.com/android-iphone-price-disparity-increase-2015-2. Accessed 28 Feb 2016
Dermatology Planet by Edizioni Scripta Manent. https://itunes.apple.com/us/app/dermatology-planet/id499547735?mt=8. Accessed 4 Feb 2016
Melanoma Watch, IOS application by Stroika. https://itunes.apple.com/gb/app/melanoma-watch/id381272252?mt=8. Accessed 4 Feb 2016
Dermlink.md, IOS application by Dermlink.Inc. https://itunes.apple.com/us/app/dermlink.md/id548549220?ls=1&mt=8. Accessed 4 Feb
iDoc24 - Ask the dermatologist today!, IOS application by iDoc24 Inc. https://itunes.apple.com/gb/app/idoc24-ask-dermatologist-today!/id481226133?mt=8. Accessed 5 Feb 2016
OnlineDermClinic, IOS application by OnlineDermClinic.com LLC. https://itunes.apple.com/us/app/onlinedermclinic/id573545197?mt=8. Accessed 5 Feb 2016
Jain, S., Jagtap, V., Pise, N.: Computer aided melanoma skin cancer detection using image processing. In: International Conference on Computer, Communication and Convergence, vol. 48, pp. 735–740 (2015)
Karargyris, A., Karargyris, O., Pantelopoulos, A.: DERMA/care: an advanced image-processing mobile application for monitoring skin cancer. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 2, pp. 1–7 (2012)
Abuzaghleh, O., Faezipour, M., Barkana, B.D.: Skincure: an innovative smartphone-based application to assist in melanoma early detection and prevention. Sig. Image Process. Int. J. 5(6), 1–15 (2014)
Bourouis, A., Zerdazi, A., Feham, M., Bouchachia, A.: M-Health: skin disease analysis system using smartphone’s camera. In: The 8th International Symposium on Intelligent Systems Techniques for Ad hoc and Wireless Sensor Networks (IST-AWSN), pp. 116–1120 (2013)
Doukas, C., Stagkopoulos, P., Kiranoudis, C.T., Maglogiannis, I.: Automated skin lesion assessment using mobile technologies and cloud platforms. In: 34th Annual International Conference of the IEEE EMBS, pp. 2444–2447 (2012)
Ramlakhan, K., Shang, Y.: A mobile automated skin lesion classification system. In: 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 138–141 (2011)
Hadi, S., Tumbelaka, B., Irawan, B., Rosadi, R.: Implementing DEWA framework for early diagnosis of melanoma. In: International Conference on Computer Science and Computational Intelligence, Procedia Computer Science, vol. 59, pp. 410–418 (2015)
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts. Microsoft Research Cambridge
OpenCV. http://opencv.org/about.html. Accessed 19 Jan 2016
Acknowledgements
We want to say special thanks to Klinik und Poliklinik für Dermatologie und Allergologie, Technische Universitat Munchen, Germany for providing the classified dataset. We would also like to thanks Dr. Asad Ali Safi and Dr. Alamgir Hossain for his continuous support and guidance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Taufiq, M.A., Hameed, N., Anjum, A., Hameed, F. (2017). m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-49655-9_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49654-2
Online ISBN: 978-3-319-49655-9
eBook Packages: Computer ScienceComputer Science (R0)