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Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review
Faculty
Computer Science
Year:
2023
Type of Publication:
ZU Hosted
Pages:
85467 - 85488
Authors:
Khalied Mohamed Hosny
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Access IEEE
Volume:
11
Keywords :
Deep Learning , Optimization-Based Methods , Skin Lesions
Abstract:
Skin cancer is a senior public health issue that could profit from computer-aided diagnosis to decrease the encumbrance of this widespread disease. Researchers have been more motivated to develop computer-aided diagnosis systems because visual examination wastes time. The initial stage in skin lesion analysis is skin lesion segmentation, which might assist in the following categorization task. It is a difficult task because sometimes the whole lesion might be the same colors, and the borders of pigment regions can be foggy. Several studies have effectively handled skin lesion segmentation; nevertheless, developing new methodologies to improve efficiency is necessary. This work thoroughly analyzes the most advanced algorithms and methods for skin lesion segmentation. The review begins with traditional segmentation techniques, followed by a brief review of skin lesion segmentation using deep learning and optimization techniques. The main objective of this work is to highlight the strengths and weaknesses of a wide range of algorithms. Additionally, it examines various commonly used datasets for skin lesions and the metrics used to evaluate the performance of these techniques.
Author Related Publications
Khalied Mohamed Hosny, "SEMANTIC REPRESENTATION OF MUSIC DATABASE USING NEW ONTOLOGY-BASED SYSTEM", Journal of Theoretical and Applied Information Technology, 2020
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Khalied Mohamed Hosny, "Building a New Semantic Social Network Using Semantic Web-Based Techniques", ِASPG, 2021
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Khalied Mohamed Hosny, "New Graphical Ultimate Processor for Mapping Relational Database to Resource Description Framework", IEEE, 2022
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Khalied Mohamed Hosny, "Fast computation of accurate Zernike moments", Springer, 2008
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Khalied Mohamed Hosny, "Accurate Computation of QPCET for Color Images in Different Coordinate Systems", SPIE, 2017
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Department Related Publications
Khalied Mohamed Hosny, "A kernel-based method for fast and accurate computation of PHT in polar coordinates", Springer, 2019
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Khalied Mohamed Hosny, "Classification of skin lesions using transfer learning and augmentation with Alex-net", PLOS ONE, 2019
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Khalied Mohamed Hosny, "Feature Extraction of Color Images Using Quaternion Moments", Springer, 2019
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Khalied Mohamed Hosny, "An efficient retrieval system for biomedical images based on radial associated laguerre moments", IEEE, 2020
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Khalied Mohamed Hosny, "CUDAQuat : new parallel framework for fast computation of quaternion moments for color images applications", Springer, 2021
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