Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image

Dewinda Julianensi Rumala, Reza Fuad Rachmadi, Anggraini Dwi Sensusiati, I Ketut Eddy Purnama
{"title":"Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image","authors":"Dewinda Julianensi Rumala, Reza Fuad Rachmadi, Anggraini Dwi Sensusiati, I Ketut Eddy Purnama","doi":"arxiv-2409.10952","DOIUrl":null,"url":null,"abstract":"Achieving high accuracy with computational efficiency in brain disease\nclassification from Magnetic Resonance Imaging (MRI) scans is challenging,\nparticularly when both coarse and fine-grained distinctions are crucial.\nCurrent deep learning methods often struggle to balance accuracy with\ncomputational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear\nConvolutional Network designed to address this issue. Unlike traditional\ndual-network bilinear models, Lite-FBCN utilizes a single-network architecture,\nsignificantly reducing computational load. Lite-FBCN leverages lightweight,\npre-trained CNNs fine-tuned to extract relevant features and incorporates a\nchannel reducer layer before bilinear pooling, minimizing feature map\ndimensionality and resulting in a compact bilinear vector. Extensive\nevaluations on cross-validation and hold-out data demonstrate that Lite-FBCN\nnot only surpasses baseline CNNs but also outperforms existing bilinear models.\nLite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and\n69.37% on hold-out data (a 3% improvement over the baseline). UMAP\nvisualizations further confirm its effectiveness in distinguishing closely\nrelated brain disease classes. Moreover, its optimal trade-off between\nperformance and computational efficiency positions Lite-FBCN as a promising\nsolution for enhancing diagnostic capabilities in resource-constrained and or\nreal-time clinical environments.","PeriodicalId":501289,"journal":{"name":"arXiv - EE - Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Image and Video Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.
Lite-FBCN:从核磁共振成像图像进行脑疾病分类的轻量级快速双线性卷积网络
在磁共振成像(MRI)扫描的脑部疾病分类中,实现高准确度和计算效率是一项挑战,尤其是当粗粒度和细粒度的区分都至关重要时。我们提出的 Lite-FBCN 是一种新型轻量级快速双线性卷积网络,旨在解决这一问题。与传统的双网络双线性模型不同,Lite-FBCN 采用单网络架构,大大降低了计算负荷。Lite-FBCN 利用轻量级的预训练 CNN 进行微调,以提取相关特征,并在双线性池化之前加入信道减速层,从而最大限度地降低了特征图的维度,并产生了一个紧凑的双线性向量。在交叉验证和保留数据上进行的广泛评估表明,Lite-FBCN 不仅超越了基准 CNN,而且优于现有的双线性模型。UMAP 可视化进一步证实了它在区分密切相关的脑部疾病类别方面的有效性。此外,Lite-FBCN 在性能和计算效率之间进行了最佳权衡,因此有望成为在资源有限和实时的临床环境中提高诊断能力的解决方案。
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
自引率
0.00%
发文量
0
×
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