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A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primar...

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Autores principales: Naeem, Ahmad, Anees, Tayyaba, Naqvi, Rizwan Ali, Loh, Woong-Kee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880689/
https://www.ncbi.nlm.nih.gov/pubmed/35207763
http://dx.doi.org/10.3390/jpm12020275
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author Naeem, Ahmad
Anees, Tayyaba
Naqvi, Rizwan Ali
Loh, Woong-Kee
author_facet Naeem, Ahmad
Anees, Tayyaba
Naqvi, Rizwan Ali
Loh, Woong-Kee
author_sort Naeem, Ahmad
collection PubMed
description Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.
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spelling pubmed-88806892022-02-26 A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis Naeem, Ahmad Anees, Tayyaba Naqvi, Rizwan Ali Loh, Woong-Kee J Pers Med Review Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis. MDPI 2022-02-13 /pmc/articles/PMC8880689/ /pubmed/35207763 http://dx.doi.org/10.3390/jpm12020275 Text en © 2022 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 Review
Naeem, Ahmad
Anees, Tayyaba
Naqvi, Rizwan Ali
Loh, Woong-Kee
A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title_full A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title_fullStr A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title_full_unstemmed A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title_short A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis
title_sort comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880689/
https://www.ncbi.nlm.nih.gov/pubmed/35207763
http://dx.doi.org/10.3390/jpm12020275
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