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Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection
Author(s) -
Divya D.,
Ganeshbabu T. R.
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22414
Subject(s) - preprocessor , computer science , artificial intelligence , segmentation , pattern recognition (psychology) , feature extraction , deep learning , artificial neural network , feature (linguistics) , image segmentation , philosophy , linguistics
This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation‐based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA‐DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.