Brain tumours disrupt the normal functioning of the brain and,if left untreated,can invade surrounding tissues,blood vessels,and nerves,posing a severe threat.Consequently,early detection is crucial to prevent tragic ...Brain tumours disrupt the normal functioning of the brain and,if left untreated,can invade surrounding tissues,blood vessels,and nerves,posing a severe threat.Consequently,early detection is crucial to prevent tragic outcomes.Distinguishing brain tumours through manual detection poses a significant challenge given their diverse features,such as differing shapes,sizes,and nucleus characteristics.Therefore,this research introduces an improved architecture for tumour detection named as Brain-RetinaNet,an extension of the RetinaNet model.Brain-RetinaNet is specifically designed for automated detection and identification of brain tumours in MRI images.It utilises an advanced multiscale feature fusion mechanism within the X-module,complemented by the channel attention module.The feature fusion mechanism within the X-module progressively merges features from different scales,producing enriched feature maps that encompass valuable information.At the same time,the attention module dynamically allocates optimal weights to individual channels within the feature map,enabling the network to prioritise relevant features while reducing interference from unnecessary ones.Moreover,this study employs data augmentation technique to address the limitation of a limited number of available samples.Experimental results indicate that Brain-RetinaNet outperforms existing detectors such as YOLO,SSD,Centernet,EfficientNet,and M2det for the brain tumour detection from MRI images.展开更多
蝴蝶是一种对栖息地敏感的昆虫,自然环境中的蝴蝶种类分布反映了区域生态系统平衡和生物多样性.专家鉴别蝴蝶种类耗时耗力,计算机视觉技术为野外环境中蝴蝶种类自动识别提供了可能.针对野外环境下的蝴蝶图像特征,提出2种新的硬注意力机...蝴蝶是一种对栖息地敏感的昆虫,自然环境中的蝴蝶种类分布反映了区域生态系统平衡和生物多样性.专家鉴别蝴蝶种类耗时耗力,计算机视觉技术为野外环境中蝴蝶种类自动识别提供了可能.针对野外环境下的蝴蝶图像特征,提出2种新的硬注意力机制,DSEA(direct squeeze-and-excitation with global average pooling)和DSEM(direct squeeze-and-excitation with global max pooling),改进经典目标检测算法RetinaNet,并引入可变形卷积增强RetinaNet对蝴蝶形变的建模能力,实现野外环境下蝴蝶种类自动识别.以mAP(mean average precision)目标检测指标评价模型性能,通过实验结果可视化,分析影响模型性能的关键因素.实验结果显示,提出的改进RetinaNet对自然环境下的蝴蝶识别任务具有很不错的效果,特别是基于DSEM的RetinaNet;分布平衡的训练集可以提升提出模型的泛化性能;样本的结构相异性是影响模型性能的关键因素.展开更多
基金funding this work through Ongoing Research Funding Program,(ORF-2025-704)King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumours disrupt the normal functioning of the brain and,if left untreated,can invade surrounding tissues,blood vessels,and nerves,posing a severe threat.Consequently,early detection is crucial to prevent tragic outcomes.Distinguishing brain tumours through manual detection poses a significant challenge given their diverse features,such as differing shapes,sizes,and nucleus characteristics.Therefore,this research introduces an improved architecture for tumour detection named as Brain-RetinaNet,an extension of the RetinaNet model.Brain-RetinaNet is specifically designed for automated detection and identification of brain tumours in MRI images.It utilises an advanced multiscale feature fusion mechanism within the X-module,complemented by the channel attention module.The feature fusion mechanism within the X-module progressively merges features from different scales,producing enriched feature maps that encompass valuable information.At the same time,the attention module dynamically allocates optimal weights to individual channels within the feature map,enabling the network to prioritise relevant features while reducing interference from unnecessary ones.Moreover,this study employs data augmentation technique to address the limitation of a limited number of available samples.Experimental results indicate that Brain-RetinaNet outperforms existing detectors such as YOLO,SSD,Centernet,EfficientNet,and M2det for the brain tumour detection from MRI images.
文摘蝴蝶是一种对栖息地敏感的昆虫,自然环境中的蝴蝶种类分布反映了区域生态系统平衡和生物多样性.专家鉴别蝴蝶种类耗时耗力,计算机视觉技术为野外环境中蝴蝶种类自动识别提供了可能.针对野外环境下的蝴蝶图像特征,提出2种新的硬注意力机制,DSEA(direct squeeze-and-excitation with global average pooling)和DSEM(direct squeeze-and-excitation with global max pooling),改进经典目标检测算法RetinaNet,并引入可变形卷积增强RetinaNet对蝴蝶形变的建模能力,实现野外环境下蝴蝶种类自动识别.以mAP(mean average precision)目标检测指标评价模型性能,通过实验结果可视化,分析影响模型性能的关键因素.实验结果显示,提出的改进RetinaNet对自然环境下的蝴蝶识别任务具有很不错的效果,特别是基于DSEM的RetinaNet;分布平衡的训练集可以提升提出模型的泛化性能;样本的结构相异性是影响模型性能的关键因素.
