Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Muthuramalingam, Sivakumar
Unknown Affiliation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Impact of federated learning and explainable artificial intelligence for medical image diagnosis Muthuramalingam, Sivakumar; Thiyagarajan, Padmapriya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3772-3785

Abstract

Medical image recognition has enormous potential to benefit from the recent developments in federated learning (FL) and interpretable artificial intelligence (AI). The function of FL and explainable artificial intelligence (XAI) in the diagnosis of brain cancers is discussed in this paper. XAI and FL techniques are vital for ensuring data ethics during medical image processing. This paper highlights the benefits of FL, such as cooperative model training and data privacy preservation, and the significance of XAI approaches in providing logical justifications for model predictions. A number of case studies on the segmentation of medical images employing FL were reviewed to compares and contrasts various methods for assessing the efficacy of FL and XAI based diagnostic models for brain tumors. The relevance of FL and XAI to improve the accuracy and interpretability during medical image diagnosis have been presented. Future research directions are also described indicating as to integrate data from various modes, create standardised evaluation processes, and manage ethical issues. This paper is intended to provide a deeper insight on relevance of FL and XAI in medical image diagnosis.
Co-Authors Thiyagarajan, Padmapriya