Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods...Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.展开更多
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e...To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.展开更多
目的探讨MRI多序列成像在肝硬化背景下小肝癌诊断和微血管侵犯评估中的应用价值。方法选择2020年9月~2022年9月在我院接受诊断的80例疑似小肝癌的患者为研究对象,均采用西门子3.0 T Verio超导型磁共振扫描仪检查,分析MRI多序列成像对肝...目的探讨MRI多序列成像在肝硬化背景下小肝癌诊断和微血管侵犯评估中的应用价值。方法选择2020年9月~2022年9月在我院接受诊断的80例疑似小肝癌的患者为研究对象,均采用西门子3.0 T Verio超导型磁共振扫描仪检查,分析MRI多序列成像对肝硬化背景下小肝癌诊断和微血管侵犯评估中的应用价值,以肝脏穿刺活检为金标准,采用Kappa一致性检验,分析MRI多序列成像在小肝癌诊断和微血管侵犯的一致性。结果80例疑似小肝癌患者经病理确诊36例,未发生肝癌44例。T2WI诊断敏感度为83.33%,特异度为79.55%,准确度为81.25%,Kappa值为0.624;T1WI诊断敏感度为77.78%,特异度为81.82%,准确度为80.00%,,Kappa值为0.596;LAVA诊断敏感度为83.33%,特异度为81.82%,准确度为82.50%,Kappa值为0.648;联合诊断敏感度为94.44%,特异度为77.27%,准确度为85.00%,Kappa值为0.703。36例疑似小肝癌患者经病理确诊MVI 7例,未发生MVI 29例。T2WI诊断敏感度为71.43%,特异度为86.21%,准确度为83.33%,Kappa值为0.520;T1WI诊断敏感度为71.43%,特异度为82.76%,准确度为80.56%,Kappa值为0.466;LAVA诊断敏感度为71.43%,特异度为89.66%,准确度为86.11%,Kappa值为0.579;联合诊断敏感度为85.71%,特异度为82.76%,准确度为83.33%,Kappa值为0.563。结论MRI多序列成像有助于提高肝硬化背景下小肝癌的诊断和微血管侵犯的评估,具有良好的应用价值。展开更多
基金Ministry of Education,Youth and Sports of the Chezk Republic,Grant/Award Numbers:SP2023/039,SP2023/042the European Union under the REFRESH,Grant/Award Number:CZ.10.03.01/00/22_003/0000048。
文摘Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
文摘To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.
文摘目的探讨MRI多序列成像在肝硬化背景下小肝癌诊断和微血管侵犯评估中的应用价值。方法选择2020年9月~2022年9月在我院接受诊断的80例疑似小肝癌的患者为研究对象,均采用西门子3.0 T Verio超导型磁共振扫描仪检查,分析MRI多序列成像对肝硬化背景下小肝癌诊断和微血管侵犯评估中的应用价值,以肝脏穿刺活检为金标准,采用Kappa一致性检验,分析MRI多序列成像在小肝癌诊断和微血管侵犯的一致性。结果80例疑似小肝癌患者经病理确诊36例,未发生肝癌44例。T2WI诊断敏感度为83.33%,特异度为79.55%,准确度为81.25%,Kappa值为0.624;T1WI诊断敏感度为77.78%,特异度为81.82%,准确度为80.00%,,Kappa值为0.596;LAVA诊断敏感度为83.33%,特异度为81.82%,准确度为82.50%,Kappa值为0.648;联合诊断敏感度为94.44%,特异度为77.27%,准确度为85.00%,Kappa值为0.703。36例疑似小肝癌患者经病理确诊MVI 7例,未发生MVI 29例。T2WI诊断敏感度为71.43%,特异度为86.21%,准确度为83.33%,Kappa值为0.520;T1WI诊断敏感度为71.43%,特异度为82.76%,准确度为80.56%,Kappa值为0.466;LAVA诊断敏感度为71.43%,特异度为89.66%,准确度为86.11%,Kappa值为0.579;联合诊断敏感度为85.71%,特异度为82.76%,准确度为83.33%,Kappa值为0.563。结论MRI多序列成像有助于提高肝硬化背景下小肝癌的诊断和微血管侵犯的评估,具有良好的应用价值。
