An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique
Ramandeep Kaur1, Gagandeep2, Parveen Kumar3, Geetanjali Babbar4
1Ramandeep Kaur, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
2Dr. Gagandeep, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
3Parveen Kumar, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
4Geetanjali Babbar, Department of Computer Science and Engineering, Chandigarh Engineering College, Landran (Mohali), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 634-639 | Volume-8 Issue-9S August 2019 | Retrieval Number: I101010789S19/19©BEIESP | DOI: 10.35940/ijitee.I1101.0789S19
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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: Scientists have been trying to implement traditional methods around the world, particularly in developing countries, to reduce the death rate of skin cancer in humans. The scientific term is named as melanoma. But this effort always working hard as the system is costly, the low availability of experts and the conventional telemedicine. There are three types of skin cancer: basal cell cancer (BCC), squamous cell cancer, and melanoma. More than 90% of human is affected by ultraviolet (UV) radiation exposed to the sun. In this research, a skin cancer detection system (BCC) is designed in MATLAB. The images going to different processes such as Pre processing, feature extraction and classification. In pre-processing K-mean clustering is applied to determine the foreground and background of an image, since some part of background appear in the image after K-mean. Therefore, to resolve this problem Particle Swarm optimization (PSO) is applied. The segmented image features are extracted using Speed Up Robust Features (SURF), this helps to enhance the quality of the image. The Artificial neural network (ANN) is trained on the basis of these extracted features. To determine the efficiency of the system, the images are tested and performance parameters are measured. The detection accuracy determined by this model is about 98.7 5 is obtained.
Keywords: Skin Cancer, K-mean, PSO, SURF, ANN.
Scope of the Article: Computer Science and Its Applications