Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic pro...Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.展开更多
利用已有的标记数据对新领域图像进行分类是遥感图像场景分类的重要研究方向。提出了一种基于半监督子空间迁移的稀疏表示(sparse representation method based on semi-supervised transfer learning subspace,SR-SSTLS)遥感图像场景...利用已有的标记数据对新领域图像进行分类是遥感图像场景分类的重要研究方向。提出了一种基于半监督子空间迁移的稀疏表示(sparse representation method based on semi-supervised transfer learning subspace,SR-SSTLS)遥感图像场景分类方法。为减少源域和目标域数据分布变化,将不同数据域的遥感图像投影至共享子空间。源域和目标域数据在投影子空间协同学习共享字典,使得带标记的源域数据辅助目标域模型的建立。同时,建立了基于源域、目标域、源域-目标域标记数据的拉普拉斯图矩阵和目标域未标记数据的拉普拉斯正则化项,使得目标域中的数据均得到很好编码。在多个遥感图像数据集上的实验结果均证明了SR-SSTLS方法的有效性。展开更多
文摘Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
文摘利用已有的标记数据对新领域图像进行分类是遥感图像场景分类的重要研究方向。提出了一种基于半监督子空间迁移的稀疏表示(sparse representation method based on semi-supervised transfer learning subspace,SR-SSTLS)遥感图像场景分类方法。为减少源域和目标域数据分布变化,将不同数据域的遥感图像投影至共享子空间。源域和目标域数据在投影子空间协同学习共享字典,使得带标记的源域数据辅助目标域模型的建立。同时,建立了基于源域、目标域、源域-目标域标记数据的拉普拉斯图矩阵和目标域未标记数据的拉普拉斯正则化项,使得目标域中的数据均得到很好编码。在多个遥感图像数据集上的实验结果均证明了SR-SSTLS方法的有效性。
