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The next generation of convolutional neural networks in neuro-oncology:A comparative analysis of performance,model complexity,and efficiency in brain tumor diagnosis 认领 引用
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作者 Ishak Pacal Muhammet Deveci 《Brain network disorders》 2026年第2期136-146,共11页
Background:Advances in convolutional neural networks(CNNs)have continued to enhance automated brain tumor diagnosis;however,the trade-off between increasing model complexity and computational efficiency remains insuff... Background:Advances in convolutional neural networks(CNNs)have continued to enhance automated brain tumor diagnosis;however,the trade-off between increasing model complexity and computational efficiency remains insufficiently explored.Methods:This study provides a comprehensive evaluation of ten model variants from the EfficientNetV2,ConvNeXt,and InceptionNeXt families using the Brain tumor Image Segmentation and Classification(BRISC)2025 benchmark dataset.Results:By examining the efficiency frontier across these architectures,we investigate how predictive performance scales relative to computational cost.All models demonstrated robust predictive performance,with accuracies ranging from 0.972 to 0.992.The InceptionNeXt-Base model achieved the highest accuracy(0.992)and an F1-score of 0.9929.Importantly,our analysis revealed a law of diminishing returns:While computationally intensive models yielded marginal accuracy improvements,efficient architectures such as EfficientNetV2-Medium achieved comparable accuracy(0.990)with substantially lower parameter counts and latency.To validate prediction reliability,gradient-weighted class activation mapping++(Grad-CAM++)interpretability analysis was employed,confirming that the top-performing models consistently focused on anatomically relevant tumor regions rather than background artifacts.Conclusion:These findings provide practical guidance for selecting resource-efficient architectures that balance high diagnostic accuracy with the computational constraints of clinical environments. 展开更多
关键词 Brain tumor classification BRISC 2025 dataset Convolutional neural networks Deep learning Magnetic resonance imaging
ABRO1研究进展 认领 引用 被引量：1
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作者 徐婧 杨晓明 张建宏 《军事医学》 CAS CSCD 北大核心 2015年第2期147-149,共3页
ABRO1(Abraxas Brother 1),也称为KIAA0157或FAM175B,是BRISC去泛素化酶复合体的一个重要组分,该复合体的主要功能是特异性剪切K-63链接的多聚泛素链。该文系统综述了ABRO1的结构特点,参与BRISC复合体组成及调节去泛素化酶活性、调控干... ABRO1(Abraxas Brother 1),也称为KIAA0157或FAM175B,是BRISC去泛素化酶复合体的一个重要组分,该复合体的主要功能是特异性剪切K-63链接的多聚泛素链。该文系统综述了ABRO1的结构特点,参与BRISC复合体组成及调节去泛素化酶活性、调控干扰素应答、参与氧化应激反应和抗心肌缺血等多方面的研究进展,为进一步研究ABRO1的生理功能及与免疫、心血管疾病的关联提供新思路。 展开更多
关键词 ABRO1 BRCC36 BRISC-SHMT 多聚泛素链
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