Article(id=1262079400946876713, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519530, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754236800000, receivedDateStr=2025-08-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832777487, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832777487, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832777487, creator=13701087609, updateTime=1778832777487, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=220, endPage=228, ext={EN=ArticleExt(id=1262079401701851437, articleId=1262079400946876713, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Research on optimizing interpretability of EfficientNet architecture brain tumor classification model, columnId=1262079400053489950, journalTitle=Electronic Measurement Technology, columnName=Information Technology and Image Processing, runingTitle=null, highlight=null, articleAbstract=
Brain tumors are highly invasive neurological diseases, and accurate early diagnosis is crucial for developing personalized treatment plans. Computer-aided diagnosis (CAD) based on deep learning techniques has achieved significant progress in medical image analysis, but limitations remain in terms of classification accuracy, computational efficiency, and interpretability. To address these issues, this study proposes an optimized EfficientNet model based on transfer learning and fine-tuning strategies. The model improves certain convolutional and fully connected layers and adds a global average pooling layer and a Dropout layer at the top of the network to enhance feature extraction capability and classification performance. Additionally, gradient-weighted class activation mapping (Grad-CAM) is introduced to visualize the model's decision-making process, effectively highlighting key discriminative regions of brain tumors, thereby improving interpretability and clinical reliability. Experimental results on the Figshare dataset demonstrate that the proposed model achieves an accuracy of 99.35% on the test set while significantly reducing parameter count and computational complexity, outperforming baseline models including VGG16, ResNet152V2, and Vision Transformer across all major metrics. Furthermore, cross-dataset validation shows that the model attains an accuracy of 92.51%, further demonstrating its robust stability and generalization capability.
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脑肿瘤是一类具有高度侵袭性的神经系统疾病，其早期准确诊断对于制定个性化治疗方案至关重要。基于深度学习的计算机辅助诊断（CAD）技术在医学成像分析中取得了显著进展，但在分类准确性、计算效率及可解释性方面仍存在不足。为此，研究提出了一种基于迁移学习和微调策略优化的EfficientNet模型，通过改进部分卷积层和全连接层，并在网络顶部添加全局平均池化层与Dropout层，以增强模型的特征提取能力与分类性能。同时，引入Grad-CAM技术实现模型决策过程可视化，有效突出脑肿瘤的关键判别特征区域，从而增强模型的可解释性与临床应用可信度。在Figshare数据集上的实验结果表明，所提模型在显著降低参数量与计算复杂度的同时，测试集上的准确率达到99.35%，主要性能指标均优于VGG16、ResNet152V2及Vision Transformer等模型。此外，跨数据集验证实验中模型的准确率达到92.51%，进一步验证了其良好的稳定性与泛化能力。
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郭心茹，硕士研究生，主要研究方向为深度学习与医学图像处理。E-mail: 470946389@qq.com
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郭心茹，硕士研究生，主要研究方向为深度学习与医学图像处理。E-mail: 470946389@qq.com
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王蕊，硕士研究生，主要研究方向为机器学习、缺陷检测。E-mail: 1392198633@qq.com
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