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All Journal Bulletin of Electrical Engineering and Informatics
Dhurgham Hassan Mahlool
Al-Qadisiyah University
Electrical & Electronics Engineering

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Distributed brain tumor diagnosis using a federated learning environment Dhurgham Hassan Mahlool; Mohamed Hamzah Abed
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4131

Abstract

In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accurate clinical findings.
Co-Authors Mohamed Hamzah Abed