Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.展开更多
Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the pre...Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the presentCOVID-19 pandemic are the early identification of COVID-19, the cautiousseparation of infected cases at the lowest cost and curing the disease in the earlystages. For that reason, the methodology adopted for this study is imaging tools,particularly computed tomography, which have been critical in diagnosing andtreating the disease. A new method for detecting Covid-19 in X-rays and CTimages has been presented based on the Scatter Wavelet Transform and DenseDeep Neural Network. The Scatter Wavelet Transform has been employed as afeature extractor, while the Dense Deep Neural Network is utilized as a binaryclassifier. An extensive experiment was carried out to evaluate the accuracy ofthe proposed method over three datasets: IEEE 80200, Kaggle, andCovid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810,respectively. The analysis of the result refers that the proposed methods achievedhigh accuracy of 98%. The proposed model results show an excellent outcomecompared to other methods in the same domain, such as (DeTraC) CNN, whichachieved only 93.1%, CNN, which achieved 94%, and stacked Multi-ResolutionCovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.展开更多
文摘Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.
文摘Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the presentCOVID-19 pandemic are the early identification of COVID-19, the cautiousseparation of infected cases at the lowest cost and curing the disease in the earlystages. For that reason, the methodology adopted for this study is imaging tools,particularly computed tomography, which have been critical in diagnosing andtreating the disease. A new method for detecting Covid-19 in X-rays and CTimages has been presented based on the Scatter Wavelet Transform and DenseDeep Neural Network. The Scatter Wavelet Transform has been employed as afeature extractor, while the Dense Deep Neural Network is utilized as a binaryclassifier. An extensive experiment was carried out to evaluate the accuracy ofthe proposed method over three datasets: IEEE 80200, Kaggle, andCovid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810,respectively. The analysis of the result refers that the proposed methods achievedhigh accuracy of 98%. The proposed model results show an excellent outcomecompared to other methods in the same domain, such as (DeTraC) CNN, whichachieved only 93.1%, CNN, which achieved 94%, and stacked Multi-ResolutionCovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.
