Author : Ankur Rao 1
Date of Publication :6th January 2021
Abstract: Eye skin cancer could be a rare malady however consistent with malignancy; it's the foremost common kind of cancer. similar to alternative varieties of cancers, it's curable for many of the cases if diagnosed properly however the method of diagnosing is sort of difficult and is that the most problematic issue within the treatment of eye skin cancer. This paper presents an automatic eye skin cancer detection technique employing a convolution neural network (CNN) and support vector machine (SVM) with victimization grey scale conversion for top image resolution. Two hundred pre-diagnosed samples square measure taken from a customary info followed by pre-processing to lower resolution samples and eventually fed to the CNN design. Though the projected technique needs a large computation, a high accuracy rate of ninety four.59% is achieved outperforming the attention willcer} detection victimization support vector machine classifier for feature classification and have extraction can implement the convolution neural network to extract options from the image.
- Scotto, Joseph, Jr JF Fraumeni, and J. A. Lee. "Melanomas of the eye and other noncutaneous sites: epidemiologic aspects." Journal of the National Cancer Institute 56, no. 3 (1976):489- 491.
- Muller, Karin, Peter JCM Nowak, Grégorius PM Luyten, Johannes P. Marijnissen, Connie de Pan, and Peter Levendag. "A modified relocatable stereotactic frame for irradiation of eye melanoma: design and evaluation of treatment accuracy." International Journal of Radiation Oncology* Biology* Physics 58, no. 1 (2004):284-291.
- Konar, Amit. Computational intelligence: principles, technique sand applications. Springer Science & Business Media, 2006.
- Naresh, Prashant, and DrRajashreeShettar. "Early detection of lung cancer using neural network techniques." Int Journal of Engineering 4 (2014):78-83
- Saini, Satish, and Ritu Vijay. "Performance analysis of artificial neural network based breast cancer detection system." International Journal of Soft Computing and Engineering 4, no. 4(2014).
- Ubaidillah, SharifahHafizahSy Ahmad, RoselinaSallehuddin,and Noorfa Haszlinna Mustaffa. "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,IsraO.,BanazierA.Ibraheem,andZeinabA. Mustafa."Detection of Eye Melanoma Using Artificial Neural Network." Journal of Clinical Engineering 43, no. 1 (2018):22-28.
- Wei, Yunchao, Wei Xia, Min Lin, JunshiHuang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan. "Hcp: A flexible cnn framework for multi-label image classification." IEEE transactions on pattern analysis and machine intelligence 38, no. 9 (2016):1901-1907
- Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015):85-117.
- New York Eye Cancer Center: https://eyecancer.com/eye- cancer/imagegalleries/image-galleries
- Acharya, U. Rajendra, Hamido Fujita, ShuLih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, and Ru San Tan. "Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals." Applied Intelligence (2018):1-12.
- Li, Yuexiang, and LinlinShen. "Skin lesion analysis towards melanoma detection using deep learning network." Sensors18, no. 2 (2018):556