Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine
- 주제(키워드) Contrast enhancement , Deep learning , ELM , Evolutionary algorithms , Fusion , Skin cancer
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold, Green Published
- 발행기관 MDPI
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191068
- 본문언어 영어
- Published As https://doi.org/10.3390/s22030799
- PubMed https://pubmed.ncbi.nlm.nih.gov/35161553
초록/요약
The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: Image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropymutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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