Justice O. Emuoyibofarhe; Daniel Ajisafe; Ronke S. Babatunde; Meinel Christoph

Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone

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Author(s)

Justice O. Emuoyibofarhe ^1,* Daniel Ajisafe ¹ Ronke S. Babatunde ² Meinel Christoph ³

1. Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso

2. Department of Computer Science, Kwara State University, Malete, Kwara State

3. Hasso Plattner Institut, University of Potsdam, Germany

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.02.04

Received: 4 Nov. 2019 / Revised: 2 Dec. 2019 / Accepted: 23 Dec. 2019 / Published: 8 Apr. 2020

Index Terms

Skin cancer, Convolutional Neural Networks, Medical Images, Android device.

Abstract

Malignant melanoma is the most dangerous kind of skin cancer. It is mostly misidentified as benign lesion. The chance of surviving melanoma disease is high if detected early. In recent years, deep convolutional neural networks have attracted great attention owing to its outstanding performance in recognizing and classifying images. This research work performs a comparative analysis of three different convolutional neural networks (CNN) trained on skin cancerous and non-cancerous images, namely: a custom 3-layer CNN, VGG-16 CNN, and Google Inception V3.
Google Inception V3 achieved the best result, with training and test accuracy of 90% and 81% respectively and a sensitivity of 84%. This work contribution is mainly in the development of an android application that uses Google Inception V3 model for early detection of skin cancer.

Cite This Paper

Justice O. Emuoyibofarhe, Daniel Ajisafe, Ronke S. Babatunde, Meinel Christoph, "Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.2, pp. 21-27, 2020. DOI:10.5815/ijieeb.2020.02.04

Reference

[1]Rogers, H. W., Weinstock, M. A., Harris, A. R., Hinckley, M. R., Feldman, S. R., Fleischer, A. B., & Coldiron, B. M. (2010). Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Archives of dermatology, 146(3), 283-287.
[2]American Cancer Society. Cancer Facts & Figures 2013. Available at: http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf. Accessibility verified April 8, 2014.
[3]Balch, C. M., Gershenwald, J. E., Soong, S. J., Thompson, J. F., Atkins, M. B., Byrd, D. R. Buzaid, A.C., Cochran, A.J., Coit, D.G., Ding, S. and Eggermont, A.M. (2009). Final version of 2009 AJCC melanoma staging and classification. Journal of clinical oncology, 27(36), 6199.
[4]Jemal, A., Siegel, R., Xu, J., & Ward, E. (2010). Cancer statistics, 2010. CA: a cancer journal for clinicians, 60(5), 277-300.
[5]AAMC Center for Workforce Studies, June 2010 Analysis. Available at: http://www.aamc.org/data. Accessibility verified April 4, 2014.
[6]World Health Organization (WHO). Third Global Forum on Human Resources https://www.who.int/mediacentre/news/releases/2013/health-workforce-shortage/en/
[7]Statista, https://www.statista.com/statistics/274774/forecast-of-mobile-phone-users-worldwide/
[8]Tsao, H., Olazagasti, J. M., Cordoro, K. M., Brewer, J. D., Taylor, S. C., Bordeaux, J. S. Chren, M.M., Sober, A.J., Tegeler, C., Bhushan, R. and Begolka, W.S (2015). Early detection of melanoma: reviewing the ABCDEs. Journal of the American Academy of Dermatology, 72(4), 717-723.
[9]Esteva, A., Kuprel, B., & Thrun, S. (2015). Deep networks for early stage skin disease and skin cancer classification. Project Report, Stanford University.
[10]Liao, H. (2016). A deep learning approach to universal skin disease classification. University of Rochester Department of Computer Science, CSC.
[11]Wadhawan, T., Situ, N., Rui, H., Lancaster, K., Yuan, X., & Zouridakis, G. (2011, August). Implementation of the 7-point checklist for melanoma detection on smart handheld devices. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 3180-3183). IEEE.
[12]Gu, Y., & Tang, J. (2014, May). A mobile system for skin cancer diagnosis and monitoring. In Mobile Multimedia/Image Processing, Security, and Applications 2014 (Vol. 9120, p. 912009). International Society for Optics and Photonics.
[13]Abuzaghleh, O., Faezipour, M., & Barkana, B. D. (2015). Skincure: An innovative smart phone-based application to assist in melanoma early detection and prevention. arXiv preprint arXiv:1501.01075.
[14]“International Skin Imaging Collaboration: Melanoma Project Website,” https://isic-archive.com/.
[15]Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. and Berg, A.C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.
[16]Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[17]Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
[18]Amir Abdi, “keras to tensorflow converter”, Github https://github.com/amir-abdi/keras_to_tensorflow
[19]Tensorflow Lite for mobile and embedded devices https://www.tensorflow.org/lite/

International Journal of Information Engineering and Electronic Business (IJIEEB)