This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the vi...This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.展开更多
Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test ...Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.展开更多
文摘This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.
基金supported by the National Key R&D Program of China(No.2016YFB1200203)the National Natural Science Foundation of China(Nos.41427806 and 61273233)
文摘Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.
