Flexible Kernel-Based Fuzzy Means Based Segmentation and Patch-Local Binary Patterns Feature Based Classification System Skin Cancer Detection

Abstract

The high death rates are occurred due to the Melanoma among the skin tumor persons. Melanoma is more dangerous when it raises inside of the skin layer. Hence, watch the wound in depth of the skin is a significant cause to identify melanoma. A (NI) non-invasive computerized dermoscopic (DS) method is introduced in these study. Existing DS system many faces various challenges includes segmentation and classification for detecting the skin cancer. The objective of the research work to improve the segmentation and classification performance. In DS images hair removal and segmentation are performed by using Hybrid Laplacian of Gaussian (HLOG) filter and Flexible Kernel-Based Fuzzy Means (FKFCM), whereas Patch-Local binary patterns (LBP) for feature extraction. The extensive experiment are conducted on largest publicly available benchmark dataset such as PH2, Kaggel and HAM 10000. To validate the performance of proposed technique when compared with traditional segmentation and classification techniques. The proposed system archive 97% of accuracy, 98% sensitivity and 96% of specificity for PH2 dataset. The planned scheme stands out among the few modern literary sources presented in the context of the analysis of DS images in terms of productivity and accepted methodologies, which proves the reliability of the novel study.

A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

“…To resolve these difficulties, several improved clustering algorithms have been proposed [ 13 , 14 , 15 , 16 ]. The K-means method divides each sample into a specific cluster.…”