新型冠状病毒是一种RNA病毒.利用人工智能技术分析新冠肺炎患者肺部图像,辅助医生快速有效地诊断与评估感染的严重程度,可以有效防止病情恶化.因此,提出一种改进的U-Net模型,在编码器部分采用预训练好的EfficientNet模型替换原有结构,...新型冠状病毒是一种RNA病毒.利用人工智能技术分析新冠肺炎患者肺部图像,辅助医生快速有效地诊断与评估感染的严重程度,可以有效防止病情恶化.因此,提出一种改进的U-Net模型,在编码器部分采用预训练好的EfficientNet模型替换原有结构,在解码器部分添加scSE(spatial and channel Squeeze&Excitation)空间注意力模块.实验结果表明,本模型在新冠肺炎CT图像分割方面优于同等参数级别的其他模型,其DSC、MIoU、ACC、SEN和SPE五个指标均高于U-Net的相应指标,可视化病灶区域分割效果良好.展开更多
Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatin...Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.展开更多
文摘新型冠状病毒是一种RNA病毒.利用人工智能技术分析新冠肺炎患者肺部图像,辅助医生快速有效地诊断与评估感染的严重程度,可以有效防止病情恶化.因此,提出一种改进的U-Net模型,在编码器部分采用预训练好的EfficientNet模型替换原有结构,在解码器部分添加scSE(spatial and channel Squeeze&Excitation)空间注意力模块.实验结果表明,本模型在新冠肺炎CT图像分割方面优于同等参数级别的其他模型,其DSC、MIoU、ACC、SEN和SPE五个指标均高于U-Net的相应指标,可视化病灶区域分割效果良好.
基金Institutional Fund Projects under Grant No.(IFPIP:801-830-1443)The author gratefully acknowledges technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.
