Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-r...Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.展开更多
In this study,renewable oil properties of Flash Point(^(0)C),Fire Point(^(0)C),Density(kg/m^(3)),Cloud Point(^(0)C),Pour Point(^(0)C),and Viscosity(cST)are predicted using image processing techniques of Red Green Blue...In this study,renewable oil properties of Flash Point(^(0)C),Fire Point(^(0)C),Density(kg/m^(3)),Cloud Point(^(0)C),Pour Point(^(0)C),and Viscosity(cST)are predicted using image processing techniques of Red Green Blue(RGB)and Hue Saturation Value(HSV).Eleven types of renewable oils are chosen for experimentation,and their surface images are captured with a high-resolution digital camera.For better accuracy,around 150 surface images are captured for each oil sample,and their average pixel data is extracted using RGB and HSV techniques.The digital pixel information(metadata)of all the oil samples is mapped to their experimental oil properties,and the accuracy of the developed metadata is validated with Fiji software due to its better image analysis and also complex data quantifying capabilities.The minimum,maximum,mean,mode and standard deviation results of RGB and HSV agree with Fiji.In addition,the developed dataset has been validated with Neural Network classification and TreeBagger algorithms.The results of TreeBagger reveal that the trained dataset is highly accurate(91.9%for RGB and 95.3%for HSV).Similarly,95.6%(RGB)and 97.3%(HSV)accuracy is achieved for Neural Network classification.Finally,two new oil surface images are trained using the developed dataset.Both RGB and HSV accurately predict the oil properties.Therefore,it is evident that predicting the significant oil properties helps optimize the production process by reducing experimental costs and time.展开更多
文摘Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.
文摘In this study,renewable oil properties of Flash Point(^(0)C),Fire Point(^(0)C),Density(kg/m^(3)),Cloud Point(^(0)C),Pour Point(^(0)C),and Viscosity(cST)are predicted using image processing techniques of Red Green Blue(RGB)and Hue Saturation Value(HSV).Eleven types of renewable oils are chosen for experimentation,and their surface images are captured with a high-resolution digital camera.For better accuracy,around 150 surface images are captured for each oil sample,and their average pixel data is extracted using RGB and HSV techniques.The digital pixel information(metadata)of all the oil samples is mapped to their experimental oil properties,and the accuracy of the developed metadata is validated with Fiji software due to its better image analysis and also complex data quantifying capabilities.The minimum,maximum,mean,mode and standard deviation results of RGB and HSV agree with Fiji.In addition,the developed dataset has been validated with Neural Network classification and TreeBagger algorithms.The results of TreeBagger reveal that the trained dataset is highly accurate(91.9%for RGB and 95.3%for HSV).Similarly,95.6%(RGB)and 97.3%(HSV)accuracy is achieved for Neural Network classification.Finally,two new oil surface images are trained using the developed dataset.Both RGB and HSV accurately predict the oil properties.Therefore,it is evident that predicting the significant oil properties helps optimize the production process by reducing experimental costs and time.
