DEF-SwinE2NET: Dual enhanced features guided with multi-model fusion for brain tumor classification using preprocessing optimization

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Muhammad Ghulam Abbas Malik , Adnan Saeed , Khurram Shehzad , Muddesar Iqbal
{"title":"DEF-SwinE2NET: Dual enhanced features guided with multi-model fusion for brain tumor classification using preprocessing optimization","authors":"Muhammad Ghulam Abbas Malik ,&nbsp;Adnan Saeed ,&nbsp;Khurram Shehzad ,&nbsp;Muddesar Iqbal","doi":"10.1016/j.bspc.2024.107079","DOIUrl":null,"url":null,"abstract":"<div><div>Brain tumors exhibit significant variability in shape, size, and location, making it difficult to achieve consistent and accurate classification. It requires advanced algorithms for handling diverse tumor presentations. To solve this issue, we propose a Dual-Enhanced Features Scheme (DEFS) with a Swin-Transformer model based on the EfficientNetV2S to improve the classification and reuse parameters. In DEFS, the dense-block with dilation enables to uncovering of hidden details and spatial relationships across varying scales in the model which are typically obscured by traditional convolutional-layers. This module is particularly crucial in medical imaging, where tumors and anomalies can present in various sizes and shapes. Further, the dual-attention mechanism in the enhanced Featured scheme enhances the explainability and interpretability of the model by using spatial and channel-wise information. Additionally, the Swin-Transformer-block improves the model’s capabilities to capture global patterns in brain-tumor images, which is highly advantageous in medical-imaging where the location and extent of abnormalities, such as tumors, can vary significantly. To strengthen the proposed DEF-SwinE2NET, we used EfficientNetV2S as a baseline-model due to its effectiveness and accurate classification compared to its predecessors. We evaluated DEFSwinE2NET using three benchmark datasets: two were sourced from Kaggle and one from a Figshare repositories. Several preprocessing-steps were applied to enhance the MRI-images before training including image cropping, median-filter noise-reduction, contrast-limited adaptive histogram equalization (CLAHE) for local-contrast enhancement, Laplacian-edge enhancement to highlight critical features, and data augmentation to improve model robustness and generalization. The DEF-SwinE2NET model achieves remarkable results with an accuracy of 99.43 %, a sensitivity of 99.39 %, and an F1-score of 99.41 %.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"100 ","pages":"Article 107079"},"PeriodicalIF":4.9000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011376","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Brain tumors exhibit significant variability in shape, size, and location, making it difficult to achieve consistent and accurate classification. It requires advanced algorithms for handling diverse tumor presentations. To solve this issue, we propose a Dual-Enhanced Features Scheme (DEFS) with a Swin-Transformer model based on the EfficientNetV2S to improve the classification and reuse parameters. In DEFS, the dense-block with dilation enables to uncovering of hidden details and spatial relationships across varying scales in the model which are typically obscured by traditional convolutional-layers. This module is particularly crucial in medical imaging, where tumors and anomalies can present in various sizes and shapes. Further, the dual-attention mechanism in the enhanced Featured scheme enhances the explainability and interpretability of the model by using spatial and channel-wise information. Additionally, the Swin-Transformer-block improves the model’s capabilities to capture global patterns in brain-tumor images, which is highly advantageous in medical-imaging where the location and extent of abnormalities, such as tumors, can vary significantly. To strengthen the proposed DEF-SwinE2NET, we used EfficientNetV2S as a baseline-model due to its effectiveness and accurate classification compared to its predecessors. We evaluated DEFSwinE2NET using three benchmark datasets: two were sourced from Kaggle and one from a Figshare repositories. Several preprocessing-steps were applied to enhance the MRI-images before training including image cropping, median-filter noise-reduction, contrast-limited adaptive histogram equalization (CLAHE) for local-contrast enhancement, Laplacian-edge enhancement to highlight critical features, and data augmentation to improve model robustness and generalization. The DEF-SwinE2NET model achieves remarkable results with an accuracy of 99.43 %, a sensitivity of 99.39 %, and an F1-score of 99.41 %.
DEF-SwinE2NET:利用预处理优化多模型融合引导脑肿瘤分类的双重增强特征
脑肿瘤在形状、大小和位置上有很大差异,因此很难实现一致而准确的分类。这需要先进的算法来处理不同的肿瘤表现。为解决这一问题,我们提出了一种基于 EfficientNetV2S 的 Swin-Transformer 模型的双增强特征方案(DEFS),以改进分类和重用参数。在 DEFS 中,具有扩张功能的密集块能够揭示模型中不同尺度的隐藏细节和空间关系,而传统卷积层通常会掩盖这些细节和关系。这一模块在医学成像中尤为重要,因为在医学成像中,肿瘤和异常点的大小和形状各不相同。此外,增强型特征方案中的双重关注机制通过使用空间和信道信息,提高了模型的可解释性和可解读性。此外,Swin-Transformer-block 还提高了模型捕捉脑肿瘤图像中全局模式的能力,这在医学影像中非常有利,因为肿瘤等异常的位置和程度可能会有很大差异。为了加强所提出的 DEF-SwinE2NET,我们使用了 EfficientNetV2S 作为基线模型,因为与前者相比,EfficientNetV2S 更有效、分类更准确。我们使用三个基准数据集对 DEFSwinE2NET 进行了评估:两个数据集来自 Kaggle,一个来自 Figshare 数据库。在训练前,我们采用了多个预处理步骤来增强核磁共振成像图像,包括图像裁剪、中值滤波降噪、用于增强局部对比度的对比度限制自适应直方图均衡化(CLAHE)、用于突出关键特征的拉普拉斯边缘增强,以及用于提高模型鲁棒性和泛化的数据增强。DEF-SwinE2NET 模型取得了显著的成果,准确率达 99.43%,灵敏度达 99.39%,F1 分数达 99.41%。
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
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信