Probabilistic Neural Network based Benign and Malignant Skin Cancer Detection

Abstract

Skin cancer is leading type of cancer which causes millions of deaths of human beings. Early identification and appropriate medications for new harmful skin malignancy cases are fundamental to guarantee a low death rate as the survival rate. Most of the related works are focusing on machine learning based algorithms, but they failed to provide the maximum accuracy and specificity. Thus, to overcome this problem, the research work is implemented with the Advanced deep learning based probabilistic neural networks (PNN) classification mechanism. Initially, k-means clustering based segmentation approach is used for efficient detection the region of skin cancer. Finally, to archive the maximum efficiency of the system, PNN developed for classification of skin cancer with the gray level co-occurrence matrix (GLCM) based Texture features; discrete wavelet transforms (DWT) based low level features and Statistical Color features respectively. Thus, the research work can be effectively used for classification of Benign and Malignant skin cancers. The simulation analysis shows that the proposed method shows better qualitative and quantitative analysis compared to the state of art approaches