Fusion of CT and MRI modalities for brain tumors classification using enhanced machine vision framework

IF 5.9 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal Pub Date : 2025-10-06 DOI:10.1016/j.asej.2025.103669

Aqib Ali , Xinde Li , Adnan Karaibrahimoğlu , Mohammad Abiad , Wali Khan Mashwani

{"title":"Fusion of CT and MRI modalities for brain tumors classification using enhanced machine vision framework","authors":"Aqib Ali , Xinde Li , Adnan Karaibrahimoğlu , Mohammad Abiad , Wali Khan Mashwani","doi":"10.1016/j.asej.2025.103669","DOIUrl":null,"url":null,"abstract":"<div><div>This study focuses on a data fusion approach for classifying brain tumors utilizing an enhanced machine vision (MV) framework. The foundation of a dataset is based on the integration of CT and MRI. We utilized the proposed hybrid segmentation approach to extract the region of interest. The hybrid feature dataset was extracted from the segmented regions and optimized via a correlation-based approach for further analysis. MV-based six classifiers were deployed: weightless neural network (WNN), averaged dependence estimator (ADE), rough set, ForEx++, CS Forest, and Multilayer Perceptron (MLP), using a 10-fold validation method. The CT-scan-based experiments observed that the MLP gives the highest (97.80%) accuracy. Similarly, the MRI-based experiments observed that the ADE performs well compared to other implemented classifiers and provides 98.13% accuracy. Lastly, the fused optimized hybrid feature dataset was utilized for experiments. Among all deployed classifiers, WNN showed a promising higher accuracy of 99.66%, respectively.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103669"},"PeriodicalIF":5.9000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447925004101","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This study focuses on a data fusion approach for classifying brain tumors utilizing an enhanced machine vision (MV) framework. The foundation of a dataset is based on the integration of CT and MRI. We utilized the proposed hybrid segmentation approach to extract the region of interest. The hybrid feature dataset was extracted from the segmented regions and optimized via a correlation-based approach for further analysis. MV-based six classifiers were deployed: weightless neural network (WNN), averaged dependence estimator (ADE), rough set, ForEx++, CS Forest, and Multilayer Perceptron (MLP), using a 10-fold validation method. The CT-scan-based experiments observed that the MLP gives the highest (97.80%) accuracy. Similarly, the MRI-based experiments observed that the ADE performs well compared to other implemented classifiers and provides 98.13% accuracy. Lastly, the fused optimized hybrid feature dataset was utilized for experiments. Among all deployed classifiers, WNN showed a promising higher accuracy of 99.66%, respectively.

查看原文本刊更多论文

利用增强的机器视觉框架融合CT和MRI模式进行脑肿瘤分类

本研究的重点是利用增强的机器视觉（MV）框架进行脑肿瘤分类的数据融合方法。数据集的基础是基于CT和MRI的融合。我们利用提出的混合分割方法提取感兴趣的区域。从分割的区域中提取混合特征数据集，并通过基于相关性的方法进行优化，以便进一步分析。使用10倍验证方法，部署了基于mv的6个分类器：无权重神经网络（WNN）、平均依赖估计器（ADE）、粗糙集、forex++、CS Forest和多层感知器（MLP）。基于ct扫描的实验发现，MLP的准确率最高（97.80%）。同样，基于mri的实验观察到，与其他实现的分类器相比，ADE表现良好，准确率达到98.13%。最后，利用融合优化后的混合特征数据集进行实验。在所有部署的分类器中，WNN分别表现出99.66%的较高准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Ain Shams Engineering Journal Engineering-General Engineering

CiteScore

10.80

自引率

13.30%

发文量

441

审稿时长

49 weeks

期刊介绍： in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.