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A novel hybrid meta...
A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation
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- Malik, Shairyar (författare)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, G.T. Road, Wah Cantonment, 47040, Pakistan
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- Islam, S. M. Riazul (författare)
- Department of Computer Science, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom
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- Akram, Tallha (författare)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, G.T. Road, Wah Cantonment, 47040, Pakistan
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- Naqvi, Syed Rameez (författare)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, G.T. Road, Wah Cantonment, 47040, Pakistan
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- Alghamdi, Norah Saleh (författare)
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
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- Baryannis, George (författare)
- Department of Computer Science, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom
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Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, GT. Road, Wah Cantonment, 47040, Pakistan Department of Computer Science, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom (creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 151
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH2, and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Bat algorithm
- Deep learning
- Differential evolution
- Skin lesion segmentation
- Naturvetenskapernas didaktik
- Science education
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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