Summary
Since melanoma spreads swiftly throughout the body, it is typically a deadly form of skin cancer. Only when skin cancer is discovered early on is it usually treatable. In order to do this, this work proposes a unique melanoma detection model that has five main phases, including (i) pre‐processing, (ii) segmentation, (iii) feature extraction, (iv) suggested HKPCA based dimensionality reduction, and (v) classification. Pre‐processing is done first, and segmentation is done using a new adaptive k‐means methodology after that. After that, features from the gray‐level co‐occurrence matrix (GLCM), deviation relevance based local binary pattern (DRLBP), and gray‐level run‐length matrix (GLRM) is extracted. Extracted features were subjected for dimensionality reduction via hybrid kernel proposed principal component analysis (HKPCA). These dimension reduced features are then classified using deep belief network (DBN) framework, where the weights will be optimized by means of improved elephant herding optimization (IEHO). Finally, a comparison of the proposed and existing models' convergent performance is conducted.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.