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

Ahmad Naeem, Tayyaba Anees, Rizwan Ali Naqvi, Woong Kee Loh

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number275
JournalJournal of Personalized Medicine
Volume12
Issue number2
DOIs
StatePublished - Feb 2022

Keywords

  • Brain tumor
  • Deep learning
  • Federated learning
  • Health care
  • Magnetic resonance imaging
  • Tumor detection
  • Tumor diagnosis

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