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Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search
Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178333/ https://www.ncbi.nlm.nih.gov/pubmed/37174970 http://dx.doi.org/10.3390/diagnostics13091579 |
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author | Dahou, Abdelghani Aseeri, Ahmad O. Mabrouk, Alhassan Ibrahim, Rehab Ali Al-Betar, Mohammed Azmi Elaziz, Mohamed Abd |
author_facet | Dahou, Abdelghani Aseeri, Ahmad O. Mabrouk, Alhassan Ibrahim, Rehab Ali Al-Betar, Mohammed Azmi Elaziz, Mohamed Abd |
author_sort | Dahou, Abdelghani |
collection | PubMed |
description | Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model’s performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features. |
format | Online Article Text |
id | pubmed-10178333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101783332023-05-13 Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search Dahou, Abdelghani Aseeri, Ahmad O. Mabrouk, Alhassan Ibrahim, Rehab Ali Al-Betar, Mohammed Azmi Elaziz, Mohamed Abd Diagnostics (Basel) Article Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model’s performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features. MDPI 2023-04-28 /pmc/articles/PMC10178333/ /pubmed/37174970 http://dx.doi.org/10.3390/diagnostics13091579 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dahou, Abdelghani Aseeri, Ahmad O. Mabrouk, Alhassan Ibrahim, Rehab Ali Al-Betar, Mohammed Azmi Elaziz, Mohamed Abd Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title | Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title_full | Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title_fullStr | Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title_full_unstemmed | Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title_short | Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search |
title_sort | optimal skin cancer detection model using transfer learning and dynamic-opposite hunger games search |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178333/ https://www.ncbi.nlm.nih.gov/pubmed/37174970 http://dx.doi.org/10.3390/diagnostics13091579 |
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