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Medical Image Classifications for 6G IoT-Enabled Smart Health Systems
As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time me...
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/PMC10000954/ https://www.ncbi.nlm.nih.gov/pubmed/36899978 http://dx.doi.org/10.3390/diagnostics13050834 |
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author | Elaziz, Mohamed Abd Dahou, Abdelghani Mabrouk, Alhassan Ibrahim, Rehab Ali Aseeri, Ahmad O. |
author_facet | Elaziz, Mohamed Abd Dahou, Abdelghani Mabrouk, Alhassan Ibrahim, Rehab Ali Aseeri, Ahmad O. |
author_sort | Elaziz, Mohamed Abd |
collection | PubMed |
description | As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA’s exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively. |
format | Online Article Text |
id | pubmed-10000954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100009542023-03-11 Medical Image Classifications for 6G IoT-Enabled Smart Health Systems Elaziz, Mohamed Abd Dahou, Abdelghani Mabrouk, Alhassan Ibrahim, Rehab Ali Aseeri, Ahmad O. Diagnostics (Basel) Article As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA’s exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively. MDPI 2023-02-22 /pmc/articles/PMC10000954/ /pubmed/36899978 http://dx.doi.org/10.3390/diagnostics13050834 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 Elaziz, Mohamed Abd Dahou, Abdelghani Mabrouk, Alhassan Ibrahim, Rehab Ali Aseeri, Ahmad O. Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title | Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title_full | Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title_fullStr | Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title_full_unstemmed | Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title_short | Medical Image Classifications for 6G IoT-Enabled Smart Health Systems |
title_sort | medical image classifications for 6g iot-enabled smart health systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000954/ https://www.ncbi.nlm.nih.gov/pubmed/36899978 http://dx.doi.org/10.3390/diagnostics13050834 |
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