Publicação
Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification
| dc.contributor.author | Taher, Fatma | |
| dc.contributor.author | Abdelaziz, Ahmed | |
| dc.contributor.institution | Information Management Research Center (MagIC) - NOVA Information Management School | |
| dc.contributor.institution | NOVA Information Management School (NOVA IMS) | |
| dc.contributor.pbl | American Scientific Publishing Group (ASPG) | |
| dc.date.accessioned | 2023-01-06T22:22:32Z | |
| dc.date.available | 2023-01-06T22:22:32Z | |
| dc.date.issued | 2022 | |
| dc.description | Taher, F., & Abdelaziz, A. (2022). Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification. International Journal of Neutrosophic Science, 19(1), 177-187. https://doi.org/10.54216/IJNS.190113 | |
| dc.description.abstract | Early detection and classification of skin lesions using dermoscopic images have attracted significant attention in the healthcare sector. Automated skin lesion segmentation becomes tedious owing to the presence of artifacts like hair, skin line, etc. Earlier works have developed skin lesion det ection models using clustering approaches. The advances in neutrosophic set (NS) models can be applied to derive effective clustering models for skin lesion segmentation. At the same time, artificial intelligence (AI) tools can be developed for the identification and categorization of skin cancer using dermoscopic images. This article introduces a Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification (NCCOML-SKSC) model. The proposed NCCOML-SKSC model derives a NCC-based segmentation approach to segment the dermoscopic images. Besides, the AlexNet model is exploited to generate a feature vector. In the final stage, the optimal multilayer perceptron (MLP) model is utilized for the classification process in which the MLP parameters are chosen by the use of a whale optimization algorithm (WOA). A detailed experimental analysis of the NCCOML-SKSC model using a benchmark dataset is performed and the results highlighted the supremacy of the NCCOML-SKSC model over the recent approaches. | en |
| dc.description.version | publishersversion | |
| dc.description.version | published | |
| dc.format.extent | 11 | |
| dc.format.extent | 594090 | |
| dc.identifier.doi | 10.54216/IJNS.190113 | |
| dc.identifier.issn | 2692-6148 | |
| dc.identifier.other | PURE: 49893793 | |
| dc.identifier.other | PURE UUID: 3aad111a-e052-48e3-9944-6029c70b34cb | |
| dc.identifier.other | Scopus: 85139564362 | |
| dc.identifier.uri | http://hdl.handle.net/10362/147111 | |
| dc.identifier.url | https://www.scopus.com/pages/publications/85139564362 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.subject | Feature Extraction | |
| dc.subject | Image segmentation | |
| dc.subject | Machine learning | |
| dc.subject | Neutrosophic set | |
| dc.subject | Whale optimization algorithm | |
| dc.subject | Mathematics (miscellaneous) | |
| dc.subject | Logic | |
| dc.subject | Applied Mathematics | |
| dc.subject | SDG 3 - Good Health and Well-being | |
| dc.title | Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification | en |
| dc.type | journal article | |
| degois.publication.firstPage | 177 | |
| degois.publication.issue | 1 | |
| degois.publication.lastPage | 187 | |
| degois.publication.title | International Journal of Neutrosophic Science | |
| degois.publication.volume | 19 | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess |
Ficheiros
Principais
1 - 1 de 1
A carregar...
- Nome:
- Neutrosophic_C_Means_Clustering_Machine_Learning_Enabled_Skin_Lesion_Segmentation_Classification.pdf
- Tamanho:
- 580.17 KB
- Formato:
- Adobe Portable Document Format