• Manikandan.S
• P Kokila

In this research, we aim to propose an intelligent automated method for identification of the type of skin lesions using machine-learning techniques. Two types of texture feature have been used to perform classification of melanoma and non-melanoma. First local information through Local Binary Pattern (LBP) on different scales and Gray Level Co-Occurrence Matrix (GLCM) at different angles has been extracted as a texture feature. Fuzzy C-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. Segmentation is performed by using Fuzzy Clustering Means because it has robust characteristics for ambiguity and can retain much more information than hard segmentation methods. Support vector machine has been used as a classifier to classify melanoma and non-melanoma. Experiments have been tested on well-known dataset dermis that is freely available on the Internet. The proposed method has been compared with state of the art methods and shows better performance in comparison to the existing methods.

Skin,cancer,malanoma,FCM,LBP

"Skin cancer detection by FCM and classification of dermoscopy images using svm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.g264-g297, May-2022, Available :http://www.jetir.org/papers/JETIR2205726.pdf

"Skin cancer detection by FCM and classification of dermoscopy images using svm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppg264-g297, May-2022, Available at : http://www.jetir.org/papers/JETIR2205726.pdf