CLOG-CD: Curriculum Learning Based on Oscillating Granularity of Class Decomposed Medical Image Classification

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Asmaa Abbas;Mohamed Medhat Gaber;Mohammed M. Abdelsamea
{"title":"CLOG-CD: Curriculum Learning Based on Oscillating Granularity of Class Decomposed Medical Image Classification","authors":"Asmaa Abbas;Mohamed Medhat Gaber;Mohammed M. Abdelsamea","doi":"10.1109/TETC.2025.3562620","DOIUrl":null,"url":null,"abstract":"Curriculum learning strategies have been proven to be effective in various applications and have gained significant interest in the field of machine learning. It has the ability to improve the final model’s performance and accelerate the training process. However, in the medical imaging domain, data irregularities can make the recognition task more challenging and usually result in misclassification between the different classes in the dataset. Class-decomposition approaches have shown promising results in solving such a problem by learning the boundaries within the classes of the data set. In this paper, we present a novel convolutional neural network (CNN) training method based on the curriculum learning strategy and the class decomposition approach, which we call <italic>CLOG-CD</i>, to improve the performance of medical image classification. We evaluated our method on four different imbalanced medical image datasets, such as Chest X-ray (CXR), brain tumour, digital knee x-ray, and histopathology colorectal cancer (CRC). <italic>CLOG-CD</i> utilises the learnt weights from the decomposition granularity of the classes, and the training is accomplished from descending to ascending order (i.e. anti-curriculum technique). We also investigated the classification performance of our proposed method based on different acceleration factors and pace function curricula. We used two pre-trained networks, ResNet-50 and DenseNet-121, as the backbone for <italic>CLOG-CD</i>. The results with ResNet-50 show that <italic>CLOG-CD</i> has the ability to improve classification performance with an accuracy of 96.08% for the CXR dataset, 96.91% for the brain tumour dataset, 79.76% for the digital knee x-ray, and 99.17% for the CRC dataset, compared to other training strategies. In addition, with DenseNet-121, <italic>CLOG-CD</i> has achieved 94.86%, 94.63%, 76.19%, and 99.45% for CXR, brain tumour, digital knee x-ray, and CRC datasets, respectively.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1043-1054"},"PeriodicalIF":5.4000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10977767/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Curriculum learning strategies have been proven to be effective in various applications and have gained significant interest in the field of machine learning. It has the ability to improve the final model’s performance and accelerate the training process. However, in the medical imaging domain, data irregularities can make the recognition task more challenging and usually result in misclassification between the different classes in the dataset. Class-decomposition approaches have shown promising results in solving such a problem by learning the boundaries within the classes of the data set. In this paper, we present a novel convolutional neural network (CNN) training method based on the curriculum learning strategy and the class decomposition approach, which we call CLOG-CD, to improve the performance of medical image classification. We evaluated our method on four different imbalanced medical image datasets, such as Chest X-ray (CXR), brain tumour, digital knee x-ray, and histopathology colorectal cancer (CRC). CLOG-CD utilises the learnt weights from the decomposition granularity of the classes, and the training is accomplished from descending to ascending order (i.e. anti-curriculum technique). We also investigated the classification performance of our proposed method based on different acceleration factors and pace function curricula. We used two pre-trained networks, ResNet-50 and DenseNet-121, as the backbone for CLOG-CD. The results with ResNet-50 show that CLOG-CD has the ability to improve classification performance with an accuracy of 96.08% for the CXR dataset, 96.91% for the brain tumour dataset, 79.76% for the digital knee x-ray, and 99.17% for the CRC dataset, compared to other training strategies. In addition, with DenseNet-121, CLOG-CD has achieved 94.86%, 94.63%, 76.19%, and 99.45% for CXR, brain tumour, digital knee x-ray, and CRC datasets, respectively.
CLOG-CD:基于类分解医学图像分类振荡粒度的课程学习
课程学习策略已被证明在各种应用中是有效的,并在机器学习领域引起了极大的兴趣。它具有提高最终模型性能和加速训练过程的能力。然而,在医学成像领域,数据不规则性会使识别任务更具挑战性,并且通常会导致数据集中不同类别之间的错误分类。类分解方法通过学习数据集类的边界,在解决这类问题方面显示出了有希望的结果。本文提出了一种基于课程学习策略和类分解方法的卷积神经网络(CNN)训练方法,我们称之为CLOG-CD,以提高医学图像分类的性能。我们在四种不同的不平衡医学图像数据集上评估了我们的方法,如胸部x线(CXR)、脑肿瘤、数字膝关节x线和组织病理学结直肠癌(CRC)。CLOG-CD利用从类的分解粒度中学习到的权值,并且从降序到升序完成训练(即反课程技术)。我们还研究了基于不同加速因子和速度功能课程的方法的分类性能。我们使用了两个预训练的网络,ResNet-50和DenseNet-121,作为CLOG-CD的主干。使用ResNet-50的结果表明,与其他训练策略相比,CLOG-CD能够提高分类性能,CXR数据集的准确率为96.08%,脑肿瘤数据集的准确率为96.91%,数字膝关节x射线数据集的准确率为79.76%,CRC数据集的准确率为99.17%。此外,使用DenseNet-121, CLOG-CD在CXR、脑肿瘤、数字膝关节x线和CRC数据集上的准确率分别达到94.86%、94.63%、76.19%和99.45%。
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
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
×
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:604180095
Book学术官方微信