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An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

Overview
Specialty Radiology
Date 2023 Apr 13
PMID 37046503
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Abstract

The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results.

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References
1.
Unver H, Ayan E . Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics (Basel). 2019; 9(3). PMC: 6787581. DOI: 10.3390/diagnostics9030072. View

2.
Joseph S, Olugbara O . Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation. Diagnostics (Basel). 2022; 12(2). PMC: 8871329. DOI: 10.3390/diagnostics12020344. View

3.
Malik S, Islam S, Akram T, Naqvi S, Alghamdi N, Baryannis G . A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation. Comput Biol Med. 2022; 151(Pt A):106222. DOI: 10.1016/j.compbiomed.2022.106222. View

4.
Sreelatha T, Subramanyam M, Giri Prasad M . Early Detection of Skin Cancer Using Melanoma Segmentation technique. J Med Syst. 2019; 43(7):190. DOI: 10.1007/s10916-019-1334-1. View

5.
Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View