Detection of Skin Cancer using Deep CNN
Hemalatha N1, Nausheeda B.S2, Athul K.P3, Navaneeth4
1N Hemalatha, Associate Professor, Department of IT Bioinformatics, AIMIT, St Aloysius College, Mangalore (Karnataka), India.
2Nausheeda B.S, Assistant Professor, Department of Information Technology, Aloysius Institute of Management and Information Technology, ST. Aloysius College, Mangalore (Karnataka), India.
3Athul K.P, Pursuing M.SC, Big Data, ST. Aloysius Institute of Management and Information Technology, Mangalore (Karnataka), India.
4Navaneeth, Pursuing MCA, ST. Aloysius Institute of Management and Information Technology, Mangalore (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 22-24 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10050285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1005.0285S20
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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: Development of abnormal cells are the cause of skin cancer that have the ability to attack or spread to various parts of the body. The skin cancer signs may include mole that has varied in size, shape, color, and may also haveno –uniform edges, might be having multiple colours, and would itch orevn bleed in some cases. The exposure to the UV-rays from the sun is considered to be accountable for more than 90% of the Skin Cancer cases which are recorded.In this paper, the development of a classificiation system for skin cancer, is discussed, using Convolutional Neural Network which would help in classifying the cancer usingTensorFlow and Keras as Malignantor Benign. The collected images from the data set are fed into the system and it is processed to classify the skin cancer. After the implementation the accuracy of the Convolutional 2-D layer system developed is found to be 78%.
Keywords: Skin Cancer, Convolutional Neural Network, Keras, Tensor Flow, Benign, Malignant.
Scope of the Article: Deep Learning