Convolutional Neural Networks Model in Premature Detection of Melanoma
Luis Chávez A.1, YudelyPalpán F.2, Ciro Rodriguez3
1Luis Chávez A., Department of Systems Engineering, National University Mayor de San Marcos, Peru.
2YudelyPalpán F., Department of Systems Engineering, National University Mayor de San Marcos, Peru.
3Ciro Rodriguez, Department of Systems Engineering, National University Mayor de San Marcos, Peru.
Manuscript received on 06 December 2019 | Revised Manuscript received on 20 December 2019 | Manuscript Published on 31 December 2019 | PP: 6-11 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L100210812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1002.10812S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski’sAlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.
Keywords: Neural Networks, Convolutional Neural Networks, Model, Melanoma, Premature Detection.
Scope of the Article: Ubiquitous Networks