An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique

• Published in: International journal of innovative technology and exploring engineering 2019-08, Vol.8 (9S), p.634-639
• Format: Article
• Language: English
• ISSN: 2278-3075; 2278-3075
• DOI: 10.35940/ijitee.I1101.0789S19

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.