Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection. Issue 3 (15th March 2020)

• Record Type: Journal Article
• Authors: Divya, D.; Ganeshbabu, T. R.
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 3(2020)
• Page Start: 731
• Page End: 752
• Publication Date: 2020-03-15
• Subjects: dermoscopic image -- melanoma skin cancer -- modified deer hunting algorithm -- optimized region growing -- recurrent neural network; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22414
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.