Affiliation:
1. Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
2. Department of Computer Science and Engineering, SR Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India
Abstract
Segmentation of skin lesions is a significant and demanding task in dermoscopy images. This paper proposes a new skin cancer recognition scheme, with: “Pre-processing, Segmentation, Feature extraction, Optimal Feature Selection and Classification”. Here, pre-processing is done with certain processes. The pre-processed images are segmented via the “Otsu Thresholding model”. The third phase is feature extraction, where Deviation Relevance-based “Local Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix (GLCM) features and Gray Level Run-Length Matrix (GLRM) features” are extracted. From these extracted features, the optimal features are chosen via Particle Updated WOA (PU-WOA) model. Subsequently, classification occurs via Optimized DCNN and NN to classify the skin lesion. To make the classification more precise, the DCNN is optimized by the introduced algorithm. The result has shown a higher accuracy of 0.998737, when compared with other extant models like IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition