利用双 U-Net 和 CNNRF 模型联合学习进行分割协同,以增强脑肿瘤分析。

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Vinay Kukreja, Ayush Dogra, Satvik Vats, Bhawna Goyal, Shiva Mehta, Rajesh Kumar Kaushal
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引用次数: 0

摘要

背景:脑肿瘤是诊断方面的一项挑战,尤其是在成像领域,正常组织和病理组织的区分必须精确。使用最新的机器学习技术将大大有助于从核磁共振成像数据中提高脑肿瘤识别的准确性。本研究论文旨在检验一种联合学习方法的效率,该方法将卷积神经网络(CNN)和随机森林(R.F.F.)等两种分类器与双 U-Net 分割联合学习。这种方法有利于对已经分类的预处理核磁共振扫描图片进行图像识别:除了使用各种数据集外,还利用联合学习来训练 CNN-RF 模型,同时考虑到数据隐私。使用中值滤波器、高斯滤波器和维纳滤波器处理核磁共振成像图像,以滤除噪声级,使特征提取过程简单高效。手术部分采用双 U-Net 布局,性能评估基于精确度、召回率、F1 分数和准确率:该模型在本地数据集上取得了优异的分类性能,CRP 很高,宏观、微观和加权平均的 CRP 从 91.28% 到 95.52%。在整个联合平均过程中,集体模型的准确率达到了 97%,优于不同客户端的 99%。数据使用方式的正确性有助于联合平均法将单个模型的见解转化为一致的全局模型,同时保持所有个人数据的私密性:联合学习框架、CNN-RF 混合模型和双 U-Net 分割的组合结构是一种用于识别脑肿瘤 MRI 图像的稳健且保护隐私的方法。本研究的结果表明,该技术有望提高脑肿瘤分类的质量,并为临床实际应用提供了一条途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis.

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 privacypreserving 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.

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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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