Title:Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis
Volume: 20
Author(s): Vinay Kukreja, Ayush Dogra, Rajesh Kumar Kaushal, Shiva Mehta*, Satvik Vats and Bhawna Goyal
Affiliation:
- Center for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Keywords:
Federated learning, Brain tumor segmentation, MRI analytic synthesis, Neural aggregation diagnostics, Convolutional neural network- (CNN), Random forest (RF).
Abstract:
Background:
Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be
precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data.
Objective
This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks
(CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on
preprocessed MRI scan pictures that have already been categorized.
Methods:
In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The
processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy
and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy.
Results:
The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and
weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to
those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert
individual model insights into a consistent global model while keeping all personal data private.
Conclusion:
The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacy-preserving
approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in
improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.
