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

被引:39
作者
Naeem, Ahmad [1 ]
Anees, Tayyaba [2 ]
Naqvi, Rizwan Ali [3 ]
Loh, Woong-Kee [4 ]
机构
[1] Univ Management & Technol, Dept Comp Sci, Lahore 54000, Pakistan
[2] Univ Management & Technol, Dept Software Engn, Lahore 54000, Pakistan
[3] Sejong Univ, Dept Unmanned Vehicle Engn, Seoul 05006, South Korea
[4] Gachon Univ, Sch Comp, Seongnam 13120, South Korea
来源
JOURNAL OF PERSONALIZED MEDICINE | 2022年 / 12卷 / 02期
基金
新加坡国家研究基金会;
关键词
brain tumor; deep learning; federated learning; tumor diagnosis; tumor detection; magnetic resonance imaging; health care; SEGMENTATION; CLASSIFICATION; IMAGES; PERFORMANCE; FEATURES; MACHINE; FUSION; MODELS;
D O I
10.3390/jpm12020275
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:24
相关论文
共 79 条
  • [1] A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned
    Abd-Ellah, Mahmoud Khaled
    Awad, Ali Ismail
    Khalaf, Ashraf A. M.
    Hamed, Hesham F. A.
    [J]. MAGNETIC RESONANCE IMAGING, 2019, 61 : 300 - 318
  • [2] Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
    Abd-Ellah, Mahmoud Khaled
    Awad, Ali Ismail
    Khalaf, Ashraf A. M.
    Hamed, Hesham F. A.
    [J]. BUILDING SUSTAINABLE HEALTH ECOSYSTEMS, 2016, 636 : 151 - 160
  • [3] Afshar P, 2019, INT CONF ACOUST SPEE, P1368, DOI 10.1109/ICASSP.2019.8683759
  • [4] Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning
    Amin, Javaria
    Sharif, Muhammad
    Gul, Nadia
    Raza, Mudassar
    Anjum, Muhammad Almas
    Nisar, Muhammad Wasif
    Bukhari, Syed Ahmad Chan
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2019, 44 (02)
  • [5] Brain tumor detection using statistical and machine learning method
    Amin, Javaria
    Sharif, Muhammad
    Raza, Mudassar
    Saba, Tanzila
    Anjum, Muhammad Almas
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 177 : 69 - 79
  • [6] A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning
    Amin, Javeria
    Sharif, Muhammad
    Yasmin, Mussarat
    Saba, Tanzila
    Anjum, Muhammad Almas
    Fernandes, Steven Lawrence
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2019, 43 (11)
  • [7] Big data analysis for brain tumor detection: Deep convolutional neural networks
    Amin, Javeria
    Sharif, Muhammad
    Yasmin, Mussarat
    Fernandes, Steven Lawrence
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 87 : 290 - 297
  • [8] [Anonymous], 2017, ISCHEMIC STROKE LESI
  • [9] [Anonymous], 2010, Int. J. Comput. Theory Eng, DOI DOI 10.7763/IJCTE.2010.V2.207
  • [10] [Anonymous], 2015, ISCHEMIC STROKE LESI