Abstract
However, eye cancer may be the rare disease that matches malignancy; it is the most prevalent form of cancer. It is curable in many of the circumstances, equivalent to the alternative types of cancer, if correctly diagnosed, but the diagnosis approach is very complex and the most troubling difficulty for eye cancer care. This document introduces an automated technique for the identification of the skin of the eye, using a neural convolution network (CNN) with a grey victimisation conversion to top picture resolution. 200 samples pre-diagnosed square measurement based on the traditional data, resized and median filtered for low resolution batter image and ultimately supplied to the Convolution Neural Network specification. Although the planned technology requires a broad calculation, a accurate high rate of 92.5% is getting to exceed careful victimisation Convolution Neural Network classifier for classification of features and the extraction of the neural network can be used to extract options from the image..
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References
Scotto, J., Fraumeni Jr., J.F., Lee, J.A.: Melanomas of the eye and other no cutaneous sites: epidemiologic aspects. J. Natl. Cancer Inst. 56(3), 489–491 (1976)
Muller, K., Nowak, P.J.C.M., Luyten, G.P.M., Marijnissen, J.P., de Pan, C., Levendag, P.: A modified relocatable stereotactic frame for irradiation of eye melanoma: design and evaluation of treatment accuracy. Int. J. Radiat. Oncol. Biol. Phys. 58(1), 284–291 (2004)
Konar, A.: Computational Intelligence: Principles Techniques and Applications. Springer, Heidelberg (2006)
Naresh, P., Shettar, R.: Early detection of lung cancer using neural network techniques. Int. J. Eng. 4, 78–83 (2014)
Saini, S., Vijay, R.: Performance analysis of artificial neural network-based breast cancer detection system. Int. J. Soft Comput. Eng. 4(4) (2014)
Ubaidillah, S.H.S.A., Sallehuddin, R., Mustaffa, N.H.: Classification of liver cancer using artificial neural network and support vector machine. In: Proceedings of International Conference on Advance in Communication Network, and Computing, pp. 1–6 (2014)
Ahmed, I.O., Ibraheem, B.A., Mustafa, Z.A.: Detection of eye melanoma using artificial neural network. J. Clin. Eng. 43(1), 22–28 (2018)
Wei, Y., et al.: HCP: a flexible cnn framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1901–1907 (2016)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
New York Eye Cancer Center. https://eyecancer.com/eyecancer/image-galleries/image-galleries
Acharya, U.., et al.: Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl. Intell. 1–12 (2018)
Li, Y., Shen, L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2), 556 (2018)
Yamada, K., Mitsui, H., Yoshida, S., Takahashi, H., Shimizu, E.: Three dimensional measurement of cancer by compound eye system. In: 2008 World Automation Congress, pp. 1–4 (2008)
Helwan, A.: ITDS: Iris tumor detection system using image processing techniques (2014)
Samant, P., Agarwal, R.: Comparative analysis of classification based algorithms for diabetes diagnosis using iris images. J. Med. Eng. Technol. 42, 35–42 (2018)
Halim, R.A., Emanuel, A.W.: A review of Iris recognition system ROI and accuracy. In: 2020 International Conference on Smart Technology and Applications (ICoSTA), pp. 1–6 (2020)
World Health Organization. Global Cancer Rates (2003). http://www.who.int/mediacenter/release/2003/pr27/enprinted. Accessed 16 Mar 2014
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Degadwala, S., Vyas, D., Dave, H.S., Patel, V., Mehta, J.N. (2022). Eye Melanoma Cancer Detection and Classification Using CNN. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_42
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DOI: https://doi.org/10.1007/978-3-030-84760-9_42
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